Using computerised decision-support systems

How to travel cheaper last minute (TopTipsNews)9 September, 2013

Computerised decision-support systems can help health professionals bring together complex information and guide management and treatment

IN THIS ARTICLE…

How computerised systems can help health professionals to make decisions Benefits and drawbacks of these systems Evaluating system effectiveness

 

Author

Dawn Dowding is professor of applied health research at the University of Leeds.

ABSTRACT

Dowding D (2013) Using computerised decision-support systems. Nursing Times; 109: 36, 23-25.

Decision support is an extension of electronic health record or electronic patient record systems. As well as enabling health professionals to look up information about individual patients stored in the system and to consult evidence-based guidance, they give advice on the treatment and management most appropriate for that patient. They are designed to help with the process of clinical decision making.

Computerised decision-support systems match patient characteristics to a computerised knowledge base to produce patient-specific assessments or recommendations. Decision support can be paper-based, but computerised systems have the advantage of being able to quickly process patient-specific information and match it to computerised decision rules or algorithms.

This article discusses the benefits and limitations of using decision-support technology, which is becoming increasingly important as the use of health information technology systems becomes more common across healthcare.

This article has been double-blind peer reviewed Figures and tables can be seen in the attached print-friendly PDF file of the

complete article in the ‘Files’ section of this page

 

5 KEY POINTS

1. Computerised decision-support systems match patient characteristics to a computerised knowledge base to produce patient-specific assessments or recommendations

2. The systems give guidance based on current evidence and best practice guidelines3. Current evidence does not give a clear indication of the benefits of using the systems4. Too little consideration is being given to how the systems will be integrated into existing

work practices5. It is essential to plan how the effectiveness of the system will be measured before it is

introduced

 

It can be argued that technology, and computerised decision-support systems (CDSSs) in particular, takes away the “art” of clinical judgement in nursing practice, and that standardised approaches to decision making remove the individualised and holistic approach to care practised by nurses. Contrary to this belief, CDSSs work by aiding clinical decision making; they cannot make decisions for health professionals, but they can provide advice and guidance on the best course of action.

Table 1 provides examples of how CDSSs can help to support clinical decision making. These are based on categorisations developed by Garg and Adhikari (2005) following a systematic review of the benefits of using CDSSs in healthcare settings. While the majority of these examples refer to use by doctors, the systems are increasingly being used by nurses taking on extended and specialist roles. A systematic review of their use in nursing carried out in 2007 found that formal evaluations were almost exclusively focused on telephone triage and assisting with dosing and appointment recommendations in anticoagulation management (Randell et al, 2007). However, it is likely that nurses use CDSSs across a much wider spectrum; for example many nurses are non-medical prescribers and may be using them to support drug dosing and prescribing.

Evidence base

One of the problems with the evidence base that is associated with CDSSs is that often studies do not measure patient outcomes, or sample sizes are too small to detect differences between groups of patients. Currently, there is no firm indication as to whether CDSSs are worth investing in, in terms of increased benefits in patient care processes or improved patient outcomes.

The trials reviewed by Garg and Adhikari (2005) indicated that some types of CDSSs clearly improve care processes. For example, a reminder system designed to assist with the recall of patients for screening and procedures improved recall rates by 75%. The authors also found the use of a CDSS improved care processes around disease management in 62% of the studies reviewed, including rates of assessments and examinations for long-term condition management such as diabetes, heart disease and asthma. However, few studies indicate that using CDSSs improves outcomes for patients. There is a similar picture for studies evaluating the benefits of CDSSs used by nurses. Randall et al (2007) systematically reviewed only 13 studies and none of these indicated improvements in either nursing care or outcomes for patients with any certainty.

At a local level, the benefits of CDSSs may be more obvious, which may explain why their use is becoming more widespread and integrated into health information technology (HIT) systems. The systems are used by nurses working for telephone triage services such as NHS Direct, NHS24 (in Scotland) and the new NHS 111 service.

Selecting and implementing CDSS Suitability

Clearly, CDSSs are not suitable for every type of healthcare decision. They are most useful in situations where health professionals need to pull together complex information from a variety of sources (such as information about individual patients and physiological measures with complex clinical guidance), and where there is enough time to make use of the system (Dowding, 2008).

Often the decision to use a particular CDSS is made at an operational level to give a “safety net” of guidance to health professionals undertaking a new role or extending their practice. This type of approach was found in a study evaluating how nurses use the systems in two areas - in one area, nurses were expected to use a CDSS to help guide the management of patients receiving warfarin therapy, in another to use a triage system to help them with decision making at a walk-in centre. In both areas, the systems had been implemented at an organisational level as a way of supporting an extended nurse role (Dowding et al, 2009a).

A number of factors must be considered when deciding whether to implement a CDSS, which will influence how successfully the system is used (Box 1).

Rationale

It is important to identify at the outset exactly what decisions need to be supported and have a clear rationale for why a CDSS will be helpful in this instance. Consideration needs to be given to whether or not this type of decision could benefit from a CDSS, and the potential impact on the patient if a decision was wrong (is it a rare but fatal event that would be prevented, or is it a decision that is made frequently but does not have a serious impact if it is wrong?).

Any current evidence that there is an issue with current clinical practice also needs to be taken into consideration, for example if local audit data shows health professionals are regularly taking decisions that do not follow evidence-based guidelines.

It is also important to clarify who will be using the CDSS and why. One of the main reasons HIT systems fail is because too little consideration is given to the needs of the system users and how a system will be integrated into their existing work practices.

Integrating technology

The technology associated with the CDSS - both hardware (the equipment needed to run it) and software (what the CDSS interface will look like) - must also be considered. Ideally CDSSs should be integrated into existing HIT systems, such as electronic patient records, so the information already stored in the database can be used as the basis for guidance and advice. This may be particularly appropriate for systems focusing on drug dosing and prescribing, as medical history, laboratory results and current prescriptions are needed.

Another consideration is how the CDSS actually interacts with the professionals using the system. Kesselheim et al (2011) highlighted the problem of “alert fatigue”, where an electronic system has too many alerts related to different decisions, or where the alert is sensitive and triggered easily, leading to health professionals ignoring alerts. It is better to have fewer and more important alerts related to decision making, rather than less important alerts that are easier to ignore.

Organisational support

Implementing any technology effectively involves organisational support. This includes being clear on why the CDSS is important, a well-defined strategy for implementing and supporting the technology as it is introduced, and the provision of education and support to staff while they are using it.

It also requires a recognition that introducing the system may lead to both intended and unintended changes in the way health professionals work. Studies have shown that the way in which CDSSs are used by nurses changes depending on their expertise (for example, Dowding et al, 2009b); this highlights a need to recognise and be flexible in terms ofwhen it would be appropriate to use a CDSS and when it would be reasonable to accept a health professional’s clinical judgement.

Evaluating effectiveness

Mechanisms in place to evaluate the system’s effectiveness once it has been introduced are vital. If the purpose of the CDSS is to improve either care processes (such as the number of patients recalled for screening) or patient outcomes (such as reducing the number of pressure ulcers) procedures must be in place to measure whether improvements are occurring. It may also be useful to monitor if and how the CDSS is being used by staff, and whether it has changed to the way individuals work in other areas that may threaten safety elsewhere in the care system.

Unintended consequences

Often the introduction of new technology can have unintended consequences such as behaviour known as “work arounds”, these occur when the new technology does not fit with the way people work, or actively increases their workload and they develop strategies to “fix” the problem or work around it. One example of this was highlighted in a study by Koppel et al (2008); nurses were observed using an electronic medication administration system to make copies of patients’ bar codes and fixing them to their clipboards to save time, rather than scanning patients at the bedside. This increased the likelihood of drugs being given to the wrong patient if they had more than one patient bar code attached to the clip board at a time.

Conclusion

There are a number of benefits to introducing CDSSs to support decision making in practice, including providing nurses and other health professionals with current research evidence to inform their decisions. However, there needs to be a clear rationale for their introduction and systems need to be in place to support implementation and monitor use once they have been introduced.

References:

Dowding D et al (2009a) Nurses’ use of computerised clinical decision support systems: a case site analysis. Journal of Clinical Nursing; 18: 8, 1159-1167.

Dowding D et al (2009b) Experience and nurses use of computerised decision support systems. Studies in health Technology and Informatics; 146, 506-510.

Dowding D (2008) Computerised decision support systems in nursing. In: Cullum N et al (eds) Evidence-based Nursing: An Introduction. Oxford: Blackwell Publishing.

Garg A, Adhikari N (2005) Effects of computerised clinical decision support systems on practitioner performance and patient outcomes. JAMA: Journal of the American Medical Association; 293: 10, 1223-1238.

Kesselheim AS et al (2011) Clinical decision support systems could be modified to reduce ‘alert fatigue’ while still minimizing the risk of litigation. Health Affairs; 30: 12, 2310-2317.

Koppel R et al (2008) Workarounds to barcode medication administration systems: their occurrences, causes, and threats to patient safety. Journal of the American Medical Informatics Association; 15, 408-423.

Randell R et al (2007) Effects of computerised decision support systems on nursing performance and patient outcomes: a systematic review. Journal of Health Services

http://www.nursingtimes.net/nursing-practice/healthcare-it/using-computerised-decision-support-systems/5063003.article

The Five Rights of Clinical Decision Support

CDS Tools Helpful for Meeting Meaningful Use

By Robert Campbell, EdD, CPHIMS, CPEHR

A 75-year-old man sits uncomfortably on an examination table as his physician informs him that he needs to get a colonoscopy. The patient, an ex-cop, barks at the physician, “Why after all these years do I need to get a colonoscopy?” The physician coolly responds, “With a paper record, I never realized that you had a family history of colon cancer, and now that your health information is being recorded in an electronic health record (EHR) system, I am being alerted that it is time for you to have a colonoscopy.” The man relents and has the procedure. A small walnut-shaped tumor is discovered in his colon, which turns out to be cancerous. The man undergoes successful chemotherapy treatment and has just celebrated another birthday. This is a best case scenario of what can happen in a world with clinical decision support.

With the advent of the “meaningful use” EHR Incentive Program, healthcare organizations, along with healthcare providers, are required to integrate clinical decision support (CDS) into their federally certified EHR systems. To provide a foundation for understanding how to develop a systematic CDS program, one should note the five “rights” of clinical decision support. In framing the discussion, CDS will be defined in terms of its relationship to meaningful use, with the most common forms outlined, and evidence provided showing where CDS has been most effective in improving quality of care.

Clinical Support Meets Meaningful Use

CDS has been defined as a “process for enhancing health-related decisions and actions with pertinent, organized clinical knowledge and patient information to improve healthcare and healthcare delivery.”1 To understand the relationship between CDS and meaningful use, it is important to comprehend, in an unconventional way, why the concept of meaningful use has come into existence. Few, if any, remember the era when the concept of standardized time zones did not exist. In that era, a person could start a journey in one town at 8 a.m. and arrive in a town two miles away at 7:50 a.m., and arrive in another four miles away at 7:55 a.m. Time was relative to location. With the creation of the concept of a time zone, time became more absolute.

Fast forward to the Pre-HITECH Meaningful Use Era, where if you asked a physician what they wanted in an electronic health record, they might respond by saying, “I would like a system that allows me to create SOAP notes.” Or “How about a system that keeps track of patients’ demographics and a list of medications they are currently taking.” Further confounding the matter was electronic health record systems developers’ insistence that their systems were just what the doctor ordered, in terms of their patient documentation needs. Meaningful use is a much-needed attempt to standardize the functionality of an electronic health record system so that, according to former ONC coordinator Farzad Mostashari, MD, the electronic record becomes less of an “electronic filing cabinet, and more of an indispensable tool by which to deliver better healthcare.”2

Under the rules for stage 2 meaningful use, both hospitals and eligible professionals must “implement five clinical decision support interventions” directly linked to four or more clinical quality measures published by the Centers for Medicare and Medicaid Services. If relevant clinical quality indicators are not applicable, a hospital or provider must implement support measures that monitor high priority health conditions such as cancer, diabetes, hypertension, and stroke. Returning to the example above, an entity wishing to adhere to this meaningful use standard can develop a clinical decision support intervention that will alert physicians when a particular patient is a candidate for colorectal screening. This type of intervention would correspond directly with the clinical quality measure NQF-0034 Colorectal Cancer Screening, which measures the percentage of adults 50 to 75 years of age who had appropriate screening for colorectal cancer. In the above example, having a colonoscopy performed would qualify as an appropriate screening measure, with the ultimate goal of having a high percentage of the practice’s patients—who are at risk for colon cancer—screened for this disease. Additionally, to demonstrate meaningful use, hospitals and eligible professionals must implement drug-drug and drug-allergy interaction checks to comply with the clinical decision support standard.

CDS Research Shows Care Improved by Tools

Over the past two decades there have been a number of studies investigating the role clinical decision support has played in improving the quality of healthcare in the United States. In the last five years, the Agency for Healthcare Research and Quality has sponsored literature reviews examining the results of these studies. One review looked at the impact CDS had on process outcomes, clinical outcomes (morbidity, mortality, length of stay, health-related quality of life), and economic outcomes.10 This literature review revealed that there is evidence showing that CDS has the greatest impact on process outcomes such as the ordering of preventive, clinical, and treatment services, along with the enhancement of user’s knowledge pertaining to a medical condition. This led the authors of Improving Outcomes with Clinical Decision Support: An Implementer’s Guide to conclude that “strong evidence now shows that CDS is effective in improving process measures across diverse academic and nonacademic settings using both commercially and locally developed systems.”11

Types of Clinical Decision Support

Although alerts are one of the most common forms of CDS, it must be noted there are many interventions that make up the current CDS toolkit. In the book “Improving Outcomes with Clinical Decision Support: An Implementer’s Guide” the authors state that CDS interventions fall into one of four categories: data entry, data review, assessment and understanding, and triggered by user task.3 For example, one of the more innovative interventions—smart forms—falls in the category of data entry, and “integrate(s) decision support into the normal tasks of seeing a patient and documenting a note.”4

Another more widely used data entry tool is the order set. An order set is a “collection of pre-formed orders”5 used “to manage a disease state or a specific procedure”6 like a hip replacement. Order sets are a key tool in the CDS arsenal because they are thought to reduce medical errors,

develop a safer healthcare environment, improve outcomes, and enhance workflow. Other interventions in the data entry category include parameter guidance, and immediate alerts.

In the “data review” category, an intervention known as the Virtual ICU is used to monitor patients in intensive care units across multiple facilities. This intervention allows remote nurses and intensivists to observe patients in real time, check vital signs, and work closely with bedside providers. Furthermore, Virtual ICU technology can make sure that doctors and nurses follow endorsed guidelines by prompting them when a “lifesaving therapy” needs to be incorporated into the patient’s care plan.7

A third category, “assessment and understanding,” is concerned with satisfying the information needs of physicians and patients as they formulate, debate, and discuss treatment options and care plans. One innovative tool in this category is the Health Level Seven (HL7) Infobutton.8 An infobutton can be used in an electronic health record and can appear next to a condition listed in a patient’s problem list, or medication in the medication list. To learn more about the condition, the physician clicks on the button and is immediately linked to an information source presenting detailed, evidence-based knowledge regarding the disease and its treatment. The same functionality applies to medications and their contraindications with other substances.

The final category, “triggered by user task,” raises awareness about events that occur outside of routine patient-specific workflows.9 These types of interventions range from an alert in an EHR, text message, or electronic mail notification regarding an abnormal test result, a prompt regarding the need for an influenza or pneumonia vaccination, or a reminder that the patient is due for a preventive test like a colonoscopy examination.

The Five “Rights” of Clinical Decision Support

With a firm understanding of clinical decision support, its various forms, and its relationship to meaningful use, the focus can turn to the five “rights” of CDS. These five rights can be used as a framework when planning to implement CDS interventions within a facility or practice, or when creating an extensive CDS program.

The five rights include:

the right information, to the right person, in the right intervention format, through the right channel, at the right time in workflow.

The Right Information

The information presented to the end-user—or in some cases, the patient—should be evidence-based, derived from a set of recognized guidelines, or based on a national performance measure. In the case of the 75-year-old colonoscopy patient, an alert is generated informing the physician that the patient needs to be screened for colon cancer. The alert is based on the performance

measure NQF-0034, which is a national measure developed by the National Committee for Quality Assurance. Furthermore, this performance measure is based on a set of guidelines developed by the American Cancer Society that stipulates who, from the general population, should be screened for colon cancer on a regular basis.

The intervention, in this instance an alert, should contain only enough information for the end user to act on. If the end user is given too much information, this may induce cognitive overload and cause them to disregard the alert. In the current example, the physician is alerted to the fact that the patient has a family history of colon cancer and they are within the threshold—patients age 50-75—of who should be screened for colon cancer. In instances where the physician would like to read the performance measure or the guidelines on which the alert is based, the channel (EHR) used to deliver the alert should make this information available via a URL or portable document format file. As a result of the alert, the physician advises the patient to have a colonoscopy performed.

Some experts recommend that healthcare organizations and practitioners who find themselves in the early stages of CDS intervention development refrain from basing interventions solely on expert opinion.12 In some cases expert opinion can be contentious. Because it may not be universally agreed upon as best practice, it may negatively influence whether an end user complies with the recommended actions forming the basis of the CDS intervention.

The Right Person

As healthcare becomes more of a team approach, it is important to make sure that the right information gets to the right person that can then take action. The right person can be a nurse, physician, physical therapist, or in some cases, a significant other. In the example above, the right person is the physician who receives the alert and advises the patient to get a colonoscopy. However, it is important to note that CDS interventions can sometimes change care team roles. For example, if the patient is resistant to advice from their physician, the information, in the form of an alert, may be best conveyed by a significant other or sibling who can use persuasion to help gain patient compliance. The important takeaway is to present information only to individuals who can take action. A common example in the health informatics literature is one where a nurse receives an order to adjust medication dosing for a patient. This type of information is problematic because the nurse has no way of knowing whether the medication dosing has already been adjusted.

The Right Intervention Format

As previously discussed, CDS may be implemented in various formats—alerts, order sets, protocols, patient monitoring systems, and infobuttons. Consequently, it becomes important for implementers to identify the issues and problems they are trying to solve and choose the best format to resolve the problem at hand. Furthermore, when developing a CDS program, implementers should create an inventory of current systems to determine which CDS tools are available, which tools need to be developed in-house, and which tools need to be purchased through a vendor. In the opening example, a practice wishes to identify patients at risk for major illnesses, and get them to adopt preventive measures. The simplest solution is an alert that non-

intrusively informs the physician of a patient’s predisposition to an illness—in this case colon cancer.

The Right Channel

In healthcare, CDS interventions can be delivered through an EHR, PHR, computerized physician order entry, an app running on a smartphone, and—if necessary—in paper form via flow-sheets, forms, and labels. In the example above, if the physician is the right person, then the EHR may be the best platform for delivering the alert. However, if a significant other is the right person, then the right platform may be a text messaging app running on a smartphone. The alert would inform the individual of the patient’s need to have a colonoscopy performed.

The Right Time in Workflow

A common problem in health information management is the desire to overlay new technology onto current clinical processes. One negative outcome of this practice is that information may be delivered to a clinician at the wrong time, or it may not be available when it is needed. A common example of this problem occurs when a physician is treating a patient who is taking aspirin. The physician temporarily loses track of this fact and begins the process of prescribing Coumadin for the patient. After entering in all the pertinent information for the prescription, the physician attempts to send the script to the pharmacy. An alert appears on the screen informing the physician that the patient is already taking aspirin and prescribing Coumadin could generate an adverse outcome.

This is an example of where information is presented at the wrong time in the clinical workflow process. It would be more advantageous for the physician to be alerted when they began typing the word Coumadin at the very start of the prescription process, not after the prescription had been entered. This highlights a very fundamental fact in the CDS implementation process, that to successfully create an intervention, the clinical processes involved must be thoroughly understood and documented so that the right information is delivered to the right person at the right time.

Closing the loop in the example from above, workflow analysis performed on the clinical process of a physical examination may reveal that a passive alert found in the patient’s electronic health record informing the physician of the patient’s need for colon cancer screening may be the best intervention to employ. An alert appearing when the physician opens the patient’s health record, and requires the physician to actively acknowledge that they have seen the alert—which requires them to click on an alert window—may not be the best intervention. This could disrupt the physician’s current workflow and consequently may not be processed at all.

Passive alerts can appear in a prominent place on the health record—a decision based on the results of the workflow analysis—and can be processed once the physician completes the physical examination. An alternative method would be when the physician closes the patient record they are given a prompt informing them that they have five patient alerts that need to be processed.

CDS Goals and Objectives

In the early stages of developing a CDS intervention, an organization would be advised to choose a goal to focus their efforts. The goal can be an organizational priority (patient satisfaction, safety, or prevention) or it can be a national priority, such as meaningful use or the CMS quality initiatives. Whatever goal is chosen, a direct link needs to be made between the goal and its potential impact on illness, death, and clinical outcomes. Once the goal is determined, a set of objectives can be defined that when achieved will provide evidence that the goal has been successfully realized.

For example, a goal could be a reduction in major illnesses in the practice’s patient census. An objective to reach this goal could be a 90 percent screening rate for patients at risk of developing colon cancer. In addition, for each objective that is defined, a baseline rate needs to be identified to determine how successful the practice has been in meeting the objective. By defining a set of goals and objectives for the development of a CDS intervention, a practice can make use of the five rights to determine the what (information), who (recipient), how (intervention), where (format), and when (workflow) for a proposed intervention.

HIM Professionals’ Role in CDS When developing a CDS intervention for an organization or practice, one of the most important tasks to be performed by an HIM professional is workflow analysis on the related clinical processes. A workflow analysis will examine the process and determine where the process can be modified and what information is needed at each step within the process. Also, the analysis would determine which intervention(s) are candidates for enhancing the process, making it safer and more efficient while providing a higher level of quality care.

Another area where an HIM professional can be involved is the implementation and use of vocabularies and classification systems used to code family histories, problem lists, symptoms, diagnoses, and medication lists. If patient information is not properly coded, CDS interventions such as alerts and reminders will not be activated if a patient meets a specific criterion.

For example, a patient who is coded as having a family history of colon cancer, whose age falls in the range of 50-75 years old, will automatically activate an alert informing their physician that they are a candidate for a colon cancer screening. If the information regarding the patient’s family is entered as a note or unstructured data, then the alert will not be activated, and the patient may not be deemed as having a greater chance of succumbing to colon cancer.

Through the use of the five rights of CDS, workflow analysis, and clinical vocabularies and coding systems, HIM professionals can play a major role in the development of a CDS intervention and in turn help improve patient care.

Notes

1. Osheroff, J.A., Teich, J.A., D. Levick et al. Improving Outcomes with Clinical Decision Support: An Implementer’s Guide. 2nd Edition. Chicago, IL: HIMSS, 2012: p. 15.

2. Ibid.

3. Ibid.4. Schnipper, J.L. et al. “‘Smart Forms in an Electronic Medical Record: Documentation-based

Clinical Decision Support to Improve Disease Management.” Journal of the American Medical Informatics Association 15, no. 4 (2008): 520.

5. Bobb, A.M., Payne, T.H., and P.A. Gross. “Viewpoint: controversies surrounding use of order sets for clinical decision support in computerized provider order entry.” Journal of the American Medical Informatics Association 14, no. 1 (2007): 130-1.

6. Ash, J.S., Stavri P.Z., and G.J. Kuperman. “A Consensus Statement on Considerations for a Successful CPOE Implementation.” Journal of the American Medical Informatics Association 10, no. 3 (2003): 232.

7. Garlock, K., J. Price. “Virtual ICU lets doctors monitor patients from afar.” Charlotte Observer (June 10, 2013).

8. Del Fiol, G. et al. “Implementations of the HL7 Context-Aware Knowledge Retrieval (‘Infobutton’) Standard: Challenges, strengths, limitations, and uptake.” Journal of Biomedical Informatics 45, no. 4 (2012): 726-35.

9. Osheroff, J.A. et al. Improving Outcomes with Clinical Decision Support: An Implementer’s Guide. 2nd Edition. Chicago, IL: HIMSS, 2012, p. 170.

10. Lobach, D. et al. Enabling Health Care Decisionmaking Through Clinical Decision Support and Knowledge Management: Evidence Report/Technology Assessment No. 203. Rockville, MD. Agency for Healthcare Research and Quality, April 2012. http://www.ncbi.nlm.nih.gov/books/NBK97318/.

11. Osheroff, J.A. et. al. Improving Outcomes with Clinical Decision Support: An Implementer’s Guide, p. 20.

12. Ibid.

Robert Campbell (CAMPBELLR@ecu.edu) is an assistant professor, health services and information management, at East Carolina University.

Article citation:Campbell, Robert. "The Five Rights of Clinical Decision Support: CDS Tools Helpful for Meeting Meaningful Use." Journal of AHIMA 84, no.10 (October 2013): 42-47.

http://library.ahima.org/xpedio/groups/public/documents/ahima/bok1_050385.hcsp?dDocName=bok1_050385

Effects of computerized clinical decision support systems on practitioner performance and patient outcomes: Methods of a decision-maker-researcher partnership systematic review

How to save money on your plane tickets (TopTipsNews)R Brian Haynes*, Nancy L Wilczynski and the Computerized Clinical Decision Support System (CCDSS) Systematic Review Team

* Corresponding author: R Brian Haynes bhaynes@mcmaster.ca

Author Affiliations

Health Information Research Unit, Department of Clinical Epidemiology and Biostatistics, McMaster University, Health Sciences Centre, 1280 Main Street West, Hamilton, Ontario, Canada

For all author emails, please log on.

Implementation Science 2010, 5:12  doi:10.1186/1748-5908-5-12

The electronic version of this article is the complete one and can be found online at: http://www.implementationscience.com/content/5/1/12

Received: 4 December 2009

Accepted: 5 February 2010

Published:

5 February 2010

© 2010 Haynes et al; licensee BioMed Central Ltd.

This is an Open Access article distributed under the terms of the Creative Commons Attribution

License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use,

distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Background

Computerized clinical decision support systems are information technology-based systems

designed to improve clinical decision-making. As with any healthcare intervention with claims to

improve process of care or patient outcomes, decision support systems should be rigorously

evaluated before widespread dissemination into clinical practice. Engaging healthcare providers

and managers in the review process may facilitate knowledge translation and uptake. The

objective of this research was to form a partnership of healthcare providers, managers, and

researchers to review randomized controlled trials assessing the effects of computerized decision

support for six clinical application areas: primary preventive care, therapeutic drug monitoring

and dosing, drug prescribing, chronic disease management, diagnostic test ordering and

interpretation, and acute care management; and to identify study characteristics that predict

benefit.

Methods

The review was undertaken by the Health Information Research Unit, McMaster University, in

partnership with Hamilton Health Sciences, the Hamilton, Niagara, Haldimand, and Brant Local

Health Integration Network, and pertinent healthcare service teams. Following agreement on

information needs and interests with decision-makers, our earlier systematic review was updated

by searching Medline, EMBASE, EBM Review databases, and Inspec, and reviewing reference

lists through 6 January 2010. Data extraction items were expanded according to input from

decision-makers. Authors of primary studies were contacted to confirm data and to provide

additional information. Eligible trials were organized according to clinical area of application.

We included randomized controlled trials that evaluated the effect on practitioner performance or

patient outcomes of patient care provided with a computerized clinical decision support system

compared with patient care without such a system.

Results

Data will be summarized using descriptive summary measures, including proportions for

categorical variables and means for continuous variables. Univariable and multivariable logistic

regression models will be used to investigate associations between outcomes of interest and

study specific covariates. When reporting results from individual studies, we will cite the

measures of association and p-values reported in the studies. If appropriate for groups of studies

with similar features, we will conduct meta-analyses.

Conclusion

A decision-maker-researcher partnership provides a model for systematic reviews that may foster

knowledge translation and uptake.

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Background

Computerized clinical decision support systems (CCDSSs) are information technology-based

systems designed to improve clinical decision-making. Characteristics of individual patients are

matched to a computerized knowledge base, and software algorithms generate patient-specific

information in the form of assessments or recommendations. As with any healthcare intervention

with claims to improve healthcare, CCDSSs should be rigorously evaluated before widespread

dissemination into clinical practice. Further, for CCDSSs that have been properly evaluated for

clinical practice effects, a process of 'knowledge translation' (KT) is needed to ensure appropriate

implementation, including both adoption if the findings are positive and foregoing adoption if the

trials are negative or indeterminate.

The Health Information Research Unit (HIRU) at McMaster University has previously

completed highly cited systematic reviews of trials of all types of CCDSSs [1-3]. The most

recent of these [1] included 87 randomized controlled trials (RCTs) and 13 non-randomized trials

of CCDSSs, published up to September 2004. This comprehensive review found some evidence

for improvement of the processes of clinical care across several types of interventions. The

evidence summarized in the review was less encouraging in documenting benefits for patients:

only 52 of the 100 trials included a measure of clinical outcomes and only seven (13%) of these

reported a statistically significant patient benefit. Further, most of the effects measured were for

'intermediate' clinical variables, such as blood pressure and cholesterol levels, rather than more

patient-important outcomes. However, most of the studies were underpowered to detect a

clinically important effect. The review assessed study research methods and, fortunately, found

study quality improved over time.

We chose an opportunity for 'KT synthesis' funding from the Canadian Institutes of Health

Research (CIHR) to update the review, partnering with our local hospital administration and

clinical staff and our regional health authority. We are in the process of updating this review and,

in view of the large number of trials and clinical applications, split it into six reviews: primary

preventive care, therapeutic drug monitoring and dosing, drug prescribing, chronic disease

management, diagnostic test ordering and interpretation, and acute care management. The timing

of this update and separation into types of application were auspicious considering the

maturation of the field of computerized decision support, the increasing availability and

sophistication of information technology in clinical settings, the increasing pace of publication of

new studies on the evaluation of CCDSSs, and the plans for major investments in information

technology (IT) and quality assurance (QA) in our local health region and elsewhere. In this

paper, we describe the methods undertaken to form a decision-maker-research partnership and

update the systematic review.

Methods

Steps involved in conducting this update are shown in Figure 1.

Figure 1. Flow diagram of steps involved in conducting this review.

Research questions

Research questions were agreed upon by the partnership (details below). For each of the six

component reviews, we will determine whether the accumulated trials for that category show

CCDSS benefits for practitioner performance or patient outcomes. Additionally, conditional on a

positive result for this first question for each component review, we will determine which

features of the successful CCDSSs lend themselves to local implementation. Thus, the primary

questions for this review are: Do CCDSSs improve practitioner performance or patient outcomes

for primary preventive care, therapeutic drug monitoring and dosing, drug prescribing, chronic

disease management, diagnostic test ordering and interpretation, and acute care management? If

so, what are the features of successful systems that lend themselves to local implementation?

CCDSSs were defined as information systems designed to improve clinical decision-making. A

standard CCDSS can be broken down into the following components. First, practitioners,

healthcare staff, or patients can manually enter patient characteristics into the computer system,

or alternatively, electronic medical records can be queried for retrieval of patient characteristics.

The characteristics of individual patients are then matched to a computerized knowledge base

(expert physician opinion or clinical practice guidelines usually form the knowledge base for a

CCDSS). Next, the software algorithms of the CCDSS use the patient information and

knowledge base to generate patient-specific information in the form of assessments

(management options or probabilities) and/or recommendations. The computer-generated

assessments or recommendations are then delivered to the healthcare provider through various

means, including a computer screen, the electronic medical record, by pager, or printouts placed

in a patient's paper chart. The healthcare provider then chooses whether or not to employ the

computer-generated recommendations.

Partnering with decision-makers

For this synthesis project, HIRU partnered with the senior administration of Hamilton Health

Sciences (HHS, one of Canada's largest hospitals), our regional health authority (the Hamilton,

Niagara, Haldimand, and Brant Local Health Integration Network (LHIN)), and clinical service

chiefs at local hospitals. The partnership recruited leading local and regional decision-makers to

inform us of the pertinent information to extract from studies from their perspectives as service

providers and managers. Our partnership model was designed to facilitate KT, that is, to engage

the decision-makers in the review process and feed the findings of the review into decisions

concerning IT applications and purchases for our health region and its large hospitals.

The partnership model has two main groups. The first group is the decision-makers from the

hospital and region and the second is the research staff at HIRU at McMaster University. Each

group has a specific role. The role of the decision-makers is to guide the review process. Two

types of decision-makers are being engaged. The first type provides overall direction. The names

and positions of these decision-makers are shown in Table 1. The second type of decision-maker

provides specific direction for each of the six clinical application areas of the systematic review.

These decision-makers are shown in Table 2. Each of these clinical service decision-makers

(shown in Table 2) is partnered with a research staff lead for each of the six component reviews.

The role of the research staff is to do the work 'in the trenches,' that is, undertake a

comprehensive literature search, extract the data, synthesize the data, plan dissemination, and

engage in the partnership. This group is comprised of physicians, pharmacists, research staff,

graduate students, and undergraduate students. The partners will continue to work together

throughout the review process.

Table 1. Name and position of decision-makers providing overall direction

Table 2. Name and position of decisions makers for each of the six clinical application areas

Both types of decision-makers were engaged early in the review process. Their support was

secured before submitting the grant application. Each decision-maker partner was required by the

funding agency, CIHR, to sign an acknowledgement page on the grant application and provide a

letter of support and curriculum vitae. Research staff in HIRU met with each of the clinical

service decision makers independently, providing them with copies of the data extraction form

used in the previous review and sample articles in their content areas, to determine what data

should be extracted from each of the included studies. Specifically, we asked them to tell us what

information from such investigations they would need when deciding about implementation of

computerized decision support.

Engaging the decision-makers at the data extraction stage was enlightening, and let us know that

decision-makers are interested in, among other things:

1. Implementation challenges, for example, how was the system put into place? Was it too

cumbersome? Was it too slow? Was it part of an electronic medical record or computerized

physician order entry system? How did it fit into existing workflow?

2. Training details, for example, how much training on the use of the CCDSS was done, by

whom, and how?

3. The evidence base, for example, if and how the evidence base for decision support was

maintained?

4. Customization, for example, was the decision support system customizable?

All of this led to richer data extraction to be undertaken for those CCDSSs that show benefit.

We continued to engage the decision-makers throughout the review process by meeting with

them once again before data analysis to discuss how best to summarize the data and to determine

how to separate the content into the six component reviews. Prior to manuscript submission,

decision-makers will be engaged in the dissemination phase, engaging in manuscript writing and

authorship of their component reviews.

Studies eligible for review

As of 13 January 2010 we started with 86 CCDSS RCTs identified in our previously published

systematic review [1] (one of the 87 RCTs from the previous review was excluded because the

CCDSS did not provide patient-specific information), and exhaustive searches that were

originally completed in September 2004 were extended and updated to 6 January 2010.

Consideration was given only to RCTs (including cluster RCTs), given that participants in

CCDSS trials generally cannot be blinded to the interventions and RCTs at least assure

protection from allocation bias. For this update, we included RCTs in any language that

compared patient care with a CCDSS to routine care without a CCDSS and evaluated clinical

performance (i.e., a measure of process of care) or a patient outcome. Additionally, to be

included in the review, the CCDSS had to provide patient-specific advice that was reviewed by a

healthcare practitioner before any clinical action. CCDSSs for all purposes were included in the

review. Studies were excluded if the system was used solely by students, only provided

summaries of patient information, provided feedback on groups of patients without individual

assessment, only provided computer-aided instruction, or was used for image analysis.

The five questions answered to determine if a study was eligible for inclusion in the review were:

1. Is this study focused on evaluating a CCDSS?

2. Is the study a randomized, parallel controlled trial (not randomized time-series) where patient

care with a CCDSS is compared to patient care without a CCDSS?

3. Is the CCDSS used by a healthcare professional-physicians, nurses, dentists, et al.-in a clinical

practice or post-graduate training (not studies involving only students and not studies directly

influencing patient decision making)?

4. Does the CCDSS provide patient-specific information in the form of assessments

(management options or probabilities) and/or recommendations to the clinicians?

5. Is clinical performance (a measure of process of care) and/or patient outcomes (on non-

simulated patients) (including any aspect of patient well-being) described?

A response of 'yes' was required for all five questions for the article to be considered for

inclusion in the review.

Finding Relevant Studies

We have previously described our methods of finding relevant studies until 2004 [1]. An

experienced librarian developed the content terms for the search filters used to identify clinical

studies of CCDSSs. We pilot tested the search strategies and modified them to ensure that they

identified known eligible articles. The search strategies used are shown in the Appendix. For this

update, we began by examining citations retrieved from Medline, EMBASE, Ovid's Evidence-

Based Medicine Reviews database (includes Cochrane Database of Systematic Reviews, ACP

Journal Club, Database of Abstracts of Reviews of Effects (DARE), Cochrane Central Register

of Controlled Trials (CENTRAL/CCTR), Cochrane Methodology Register (CMR), Health

Technology Assessments (HTA), and NHS Economic Evaluation Database (NHSEED)), and

Inspec bibliographic database from 1 January 2004 to 6 January 2010. The search update was

initially conducted from January 1, 2004 to December 8, 2008, and subsequently to January 6,

2010. The numbers of citations retrieved from each database are shown in the Appendix. All

citations were uploaded into an in-house literature evaluation software system.

Pairs of reviewers independently evaluated the eligibility of all studies identified in our search.

Disagreements were resolved by a third reviewer. Full-text articles were retrieved for articles

where there was a disagreement. Supplementary methods of finding studies included a review of

included article reference lists, reviewing the reference lists of relevant review articles, and

searching KT+ http://plus.mcmaster.ca/kt/ webcite and EvidenceUpdates

http://plus.mcmaster.ca/EvidenceUpdates/ webcite, two databases powered by McMaster PLUS

[4]. The flow diagram of included and excluded articles is shown in Figure 2.

Figure 2. Flow diagram of included and excluded studies for the update January 1, 2004 to December 8, 2008 as of January 13, 2010 (Number for the further update to January 6, 2010 will appear in the individual clinical application results papers).

Reviewer agreement on study eligibility was quantified using the unweighted Cohen κ [5]. The

kappa was κ = 0.84 (95% confidence interval [CI], 0.82 to 0.86) for pre-adjudicated pair-wise

assessments of in/in and in/uncertain versus out/out, out/uncertain, and uncertain/uncertain.

Disagreements were then adjudicated by a third observer.

Data Extraction

Pairs of reviewers independently extracted the following data from all studies meeting eligibility

criteria: study setting, study methods, CCDSS characteristics, patient/provider characteristics,

and outcomes. Disagreements were resolved by a third reviewer or by consensus. We attempted

to contact primary authors of all included studies via email to confirm data and provide missing

data. Primary authors were sent up to two email messages where they were asked to review and

amend, if necessary, the data extracted on their study. Primary authors were presented with a

URL in the email message. When they clicked on the URL, they were presented with an on-line

web-based data extraction form that showed the data extracted on their study. Comments buttons

were available for each question and were used by authors to suggest a change or provide

clarification for a data extraction item. Upon submitting the form, an email was sent to a research

assistant in HIRU summarizing the author's responses. Changes were made to the extraction

form noting that the information came from the primary author. We sent email correspondence to

the authors of all included trials (n = 168 as of January 13, 2010) and, thus far, 119 (71%)

provided additional information or confirmed the accuracy of extracted data. When authors did

not respond or could not be contracted, a reviewer trained in data extraction reviewed the

extraction form against the full-text of the article as a final check.

All studies were scored for methodological quality on a 10-point scale consisting of five

potential sources of bias. The scale used in this update differs from the scale used in the

previously published review because only RCTs are included in this update. The scale we used is

an extension of the Jadad scale [6] (which assesses randomization, blinding, and accountability

of all patients), and includes three additional potential sources of bias (i.e., concealment of

allocation, unit of allocation, and presence of baseline differences). In brief, we considered

concealment of allocation (concealed, score = 2, versus unclear if concealed, 1, versus not

concealed, 0), the unit of allocation (a cluster such as a practice, 2, versus physician, 1, versus

patient, 0), the presence of baseline differences between the groups that were potentially linked

to study outcomes (no baseline differences present or appropriate statistical adjustments made for

differences, 2, versus baseline differences present and no statistical adjustments made, 1, versus

baseline characteristics not reported, 0), the objectivity of the outcome (objective outcomes or

subjective outcomes with blinded assessment, 2, versus subjective outcomes with no blinding but

clearly defined assessment criteria, 1, versus, subjective outcomes with no blinding and poorly

defined, 0), and the completeness of follow-up for the appropriate unit of analysis (>90%, 2,

versus 80 to 90%, 1, versus <80% or not described, 0). The unit of allocation was included

because of the possibility of group contamination in trials in which the patients of an individual

clinician could be allocated to the intervention and control groups, and the clinician would then

receive decision support for some patients but not others. Contamination bias would lead to

underestimating the effect of a CCDSS.

Data Synthesis

CCDSS and study characteristics predicting success will be analyzed and interpreted with the

study as the unit of analysis. Data will be summarized using descriptive summary measures,

including proportions for categorical variables and means (±SD, standard deviation) for

continuous variables. Univariable and multivariable logistic regression models, adjusted for

study methodological quality, will be used to investigate associations between the outcomes of

interest and study specific covariates. All analyses will be carried out using SPSS, version 18.0.

We will interpret p ≤ 0.05 as indicating statistical significance; all p-values will be two-sided.

When reporting results from individual studies, we will cite the measures of association and p-

values reported in the studies. If appropriate for groups of studies with similar features, we will

conduct meta-analyses using standard techniques, as described in the Cochrane Handbook

http://www.cochrane.org/resources/handbook/ webcite.

Conclusion

A decision-maker-researcher partnership provides a model for systematic reviews that may foster

KT and uptake.

Competing interests

The authors declare that they have no competing interests.

Authors' contributions

This paper is based on the protocol submitted for peer review funding. RBH and NLW

collaborated on this paper. Members of the Computerized Clinical Decision Support System

(CCDSS) Systematic Review Team reviewed the manuscript and provided feedback. All authors

read and approved the final manuscript.

Appendix

Databases searched from 1 January 2004 to 6 January 2010:

Medline - Ovid

Search Strategy

1. (exp artificial intelligence/NOT robotics/) OR decision making, computer-assisted/OR

diagnosis, computer-assisted/OR therapy, computer-assisted/OR decision support systems,

clinical/OR hospital information systems/OR point-of-care systems/OR computers, handheld/ut

OR decision support:.tw. OR reminder systems.sh.

2. (clinical trial.mp. OR clinical trial.pt. OR random:.mp. OR tu.xs. OR search:.tw. OR meta

analysis.mp,pt. OR review.pt. OR associated.tw. OR review.tw. OR overview.tw.) NOT

(animals.sh. OR letter.pt. OR editorial.pt.)

3. 1 AND 2

4. limit 3 to yr = '2004-current'

Total number of citations downloaded as of January 13, 2010 = 7,578 (6,430 citations retrieved

when conducting the search from January 1, 2004 to December 8, 2008; 1,148 citations retrieved

when further updating the search to January 6, 2010)

EMBASE - Ovid

Search Strategy

1. computer assisted diagnosis/OR exp computer assisted therapy/OR computer assisted drug

therapy/OR artificial intelligence/OR decision support systems, clinical/OR decision making,

computer assisted/OR hospital information systems/OR neural networks/OR expert systems/OR

computer assisted radiotherapy/OR medical information system/OR decision support:.tw.

2. random:.tw. OR clinical trial:.mp. OR exp health care quality

3. 1 AND 2

4. 3 NOT animal.sh.

5. 4 NOT letter.pt.

6. 5 NOT editorial.pt.

7. limit 6 to yr ='2004-current'

Total number of citations downloaded as of January 13, 2010 = 5,165 (4,406 citations retrieved

when conducting the search from January 1, 2004 to December 8, 2008; 759 citations retrieved

when further updating the search to January 6, 2010)

All EBM Reviews - Ovid - Includes Cochrane Database of Systematic Reviews, ACP Journal Club, DARE, CCTR, CMR, HTA, and NHSEED

Search Strategy

1. (computer-assisted and drug therapy).mp.

2. (computer-assisted and diagnosis).mp.

3. (expert and system).mp.

4. (computer and diagnosis).mp

5. (computer-assisted and decision).mp.

6. (computer and drug-therapy).mp.

7. (computer and therapy).mp.

8. (information and systems).mp.

9. (computer and decision).mp.

10. decision making, computer-assisted.mp.

11. decision support systems, clinical.mp.

12. CDSS.mp.

13. CCDSS.mp.

14. clinical decision support system:.mp.

15. (comput: assisted adj2 therapy).mp.

16. comput: assisted diagnosis.mp.

17. hospital information system:.mp.

18. point of care system:.mp.

19. (reminder system: and comput:).tw.

20. comput: assisted decision.mp.

21. comput: decision aid.mp.

22. comput: decision making.mp.

23. decision support.mp.

24. (comput: and decision support:).mp.

25. 1 OR 2 OR 3 OR 4 OR 5 OR 6 OR 7 OR 8 OR 9 OR 10 OR 11 OR 12 OR 13 OR 14 OR 15

OR 16 OR 17 OR 18 OR 19 OR 20 OR 21 OR 22 OR 23 OR 24

26. limit 25 to yr = '2004-current'

Total number of citations downloaded as of January 13, 2010 after excluding citations retrieved

from Cochrane Database of Systematic Reviews, DARE, CMR, HTA, and NHSEED = 1,964

(1,573 citations retrieved when conducting the search from January 1, 2004 to December 8,

2008; 391 citations retrieved when further updating the search to January 6, 2010)

INSPEC - Scholars Portal

Search Strategy

1. EXPERT

2. SYSTEM?

3. 1 AND 2

4. EVALUAT?

5. 3 AND 4

6. MEDICAL OR CLINICAL OR MEDIC?

7. 5 AND 6

8. PY = 2004:2010

9. 7 AND 8

Total number of citations downloaded as of January 13, 2010 = 87 (84 citations retrieved when

conducting the search from January 1, 2004 to December 8, 2008; 3 citations retrieved when

further updating the search to January 6, 2010)

Acknowledgements

The research was funded by a Canadian Institutes of Health Research Synthesis Grant:

Knowledge Translation KRS 91791. The members of the Computerized Clinical Decision

Support System (CCDSS) Systematic Review Team are: Principal Investigator, R Brian Haynes,

McMaster University and Hamilton Health Sciences (HHS), bhaynes@mcmaster.ca; Co-

Investigators, Amit X Garg, University of Western Ontario, Amit.Garg@lhsc.on.ca and K Ann

McKibbon, McMaster University, mckib@mcmaster.ca; Co-Applicants/Senior Management

Decision-makers, Murray Glendining, HHS, glendining@HHSC.CA, Rob Lloyd, HHS,

lloydrob@HHSC.CA, Akbar Panju, HHS, Panju@HHSC.CA, Teresa Smith, HHS,

smithter@HHSC.CA, Chris Probst, HHS, probst@hhsc.ca and Wendy Gerrie, HHS,

gerriew@hhsc.ca; Co-Applicants/Clinical Service Decision-Makers, Rolf Sebaldt, McMaster

University and St Joseph's Hospital, sebaldt@mcmaster.ca, Stuart Connolly, McMaster

University and HHS, connostu@ccc.mcmaster.ca, Anne Holbrook, McMaster University and

HHS, holbrook@mcmaster.ca, Marita Tonkin, HHS, tonkimar@HHSC.CA, Hertzel Gerstein,

McMaster University and HHS, gerstein@mcmaster.ca, David Koff, McMaster University and

HHS, dkoff@mcmaster.ca, John You, McMaster University and HHS, jyou@mcmaster.ca and

Rob Lloyd, HHS, lloydrob@HHSC.CA; Research Staff, Nancy L Wilczynski, McMaster

University, wilczyn@mcmaster.ca, Tamara Navarro, McMaster University,

navarro@mcmaster.ca, Jean Mackay, McMaster University, mackayj@mcmaster.ca, Lori Weise-

Kelly, McMaster University, kellyla@mcmaster.ca, Nathan Souza, McMaster University,

souzanm@mcmaster.ca, Brian Hemens, McMaster University, hemensbj@mcmaster.ca, Robby

Nieuwlaat, McMaster University, Robby.Nieuwlaat@phri.ca, Shikha Misra, McMaster

University, misrashikha@gmail.com, Jasmine Dhaliwal, McMaster University,

jasmine.dhaliwal@learnlink.mcmaster.ca, Navdeep Sahota, McMaster University,

navdeep_27@hotmail.com, Anita Ramakrishna, McMaster University,

anita.ramakrishna@learnlink.mcmaster.ca, Pavel Roshanov, McMaster University,

pavelroshanov@gmail.com, Tahany Awad, McMaster University, tahany@ctu.rh.dk, Chris

Cotoi, McMaster University, cotoic@mcmaster.ca and Nicholas Hobson, McMaster University,

hobson@mcmaster.ca.

References

1. Garg AX, Adhikari NK, McDonald H, Rosas-Arellano MP, Devereaux PJ, Beyene J, Sam J, Haynes RB: Effects of computerized clinical decision support systems on practitioner performance and patient outcomes: a systematic review.

JAMA 2005, 293:1223-1238. PubMed   Abstract | Publisher   Full   Text

2. Hunty DL, Haynes RB, Hanna SE, Smith K: Effects of computer-based clinical decision support systems on physician performance and patient outcomes: a systematic review.

JAMA 1998, 280:1339-1346. PubMed   Abstract | Publisher   Full   Text

3. Johnstony ME, Langton KB, Haynes RB, Mathieu A: Effects of computer-based clinical decision support systems on clinical performance and patient outcomes. A critical appraisal of research.

Ann Intern Med 1994, 120:135-142. PubMed   Abstract | Publisher   Full   Text

4. Haynes RB, Cotoi C, Holland J, Walters L, Wilczynski N, Jedraszewski D, McKinlay J, Parrish R, McKibbon KA, McMaster Premium Literature Service (PLUS) Project: Second-order peer review of the medical literature for clinical practitioners.

JAMA 2006, 295:1801-1808. PubMed   Abstract | Publisher   Full   Text

5. Fleiss J: Statistical methods for rates and proportions. 2nd edition. New York: Wiley-Interscience; 1981.

6. Jadad AR, Moore RA, Carroll D, Jenkinson C, Reynolds DJ, Gavaghan DJ, McQuay HJ: Assessing the quality of reports of randomized controlled trials: is blinding necessary?

Control Clin Trials 1996, 17:1-12. PubMed   Abstract | Publisher   Full   Text

http://www.implementationscience.com/content/5/1/12

What Is Clinical Decision   Support?

Posted on May 29, 2012 by Mary Griskewicz MS, FHIMSS

Many clinicians are still unclear what clinical decision support (CDS) means. Eligible Professionals (EPs) in medical practices who have attested, or are in process of attesting, to the Medicare and Medicaid EHR Incentive Programs, and thus, demonstrate meaningful use of certified EHR technology, should already know that they must attest to meeting the Stage 1 menu criteria related to CDS.  That means they must implement one clinical decision support rule, and be able to track compliance.

For example, I am often asked if e-prescribing is considered as clinical decision support. The short answer to the question is “no.”

According to a recent study (April 23, 2012) conducted by the Agency for Research Health and Quality, the authors  concluded, “Both commercially and locally developed Clinical Decision Support Systems (CDSSs) are effective at improving health care process measures across diverse settings, but evidence for clinical, economic, workload, and efficiency outcomes remains sparse.”

That is helpful, but it’s still unclear what really makes up CDS.

__________________________________________________________________

According to HIMSS, “Clinical Decision Support” is a process for enhancing health-related decisions and actions with pertinent, organized clinical knowledge and patient information to improve health and healthcare delivery.

Information recipients can include:

Patients, Clinicians, and Others involved in patient care delivery.

Information delivered can include:

General clinical knowledge and guidance, Intelligently processed patient data, or A mixture of both.

Information delivery formats can be drawn from a rich palette of options that includes:

Data and order entry facilitators, Filtered data displays, Reference information, alerts and others.

 __________________________________________________________________

HIMSS provides the following example of CDS in its CDS Scenarios 101 site for a primary care physician’s office: You are a primary care physician in a large group practice that uses an electronic health record. At the beginning of each visit, you view a dashboard of preventive care measures – like flu vaccine, colon cancer screening, cholesterol tests – that are due for your patient, based on age, medical history (problem list), and medication list stored in the EHR.

If you’d like to learn more, the HIMSS website has built some valuable resources for clinicians and the industry with case studies and an updated book on CDS.

The second edition of this authoritative, best-seller, Improving Outcomes with Clinical Decision Support: An Implementer’s Guide, has been substantially enhanced with expanded and updated guidance on using CDS interventions to improve care delivery and outcomes in diverse care settings. The book is available at the          HIMSS online store.

The new edition is more reader-friendly than the earlier version with sections that help:

1. Set up (or refine) a successful CDS program in a hospital, health system or physician practice; and

2. Configure and launch specific CDS interventions that recipients appreciate and  measurably improve targeted outcomes.

Two detailed case studies illustrate key points showing how a “real-life” CDS program—and specific CDS interventions—might evolve in a hypothetical community hospital and small physician practice.

I like the book because it provides readers with enhanced worksheets—including sample data—that help readers document and use information needed for their CDS program and interventions.  Sections in each chapter present considerations for health IT software suppliers to effectively support their clients implementing CDS. 

HIMSS resources provide eligible professionals with actual examples to help them not only meet the Stage 1 meaningful use criteria, but also, to build the needed evidence for improved clinical, economic, workload and efficiency outcomes.

Now, back to my original question – and answer – on defining clinical decision support. I hope you will agree with me, if done correctly, CDS can help providers deliver safe and efficient care for patients.

http://blog.himss.org/2012/05/29/what-is-clinical-decision-support/

CLINICAL DECISION SUPPORT SYSTEMS

Top tips to choose the right bank for your student loan (TopTipsNews)

Fahhad Farukhi

INTRODUCTION

The health care market is the largest industry in the United States, with expenditures expected

to reach $2.6 trillion, or 15.9% of the national GDP by 2010 [1]. In addition to the rapid increase in

health care expenditures, technology has developed to provide support to health care delivery staff.

The primary care delivery entity is the physician or the specialist. Throughout the evolution of medical

technology, the development of an efficient, useful, and practical clinical information system has

become a significant focus in the vendor market. However, the resistance to the implementation of

helpful technology that is common within the health care market has limited the maximization of the

potential of clinical information systems. Through the satisfaction of aggregate physician needs in

conjunction with the needs of health care managers, the implementation of a clinical information

system can be advantageous to the health care delivery process.

 

Why create a CIS?

The creation of a clinical information system

requires immense financial and intellectual

investment.  Therefore, the justifications for

such an investment must be quantitatively and

qualitatively measurable upon implementation.

The development of the clinical information

system arose from needs within the health care

field, from health care managers, quality

control, and care providers, such as physicians. 

The needs that motivated the development of

clinical information systems include; clinical

task support, clinical management control,

competition support, and clinical decision

support [2].

The primary source of support needed is in the clinical task support domain. Through the

development of the electronic medical record, the maintenance of the patient record will be simplified,

providing information support to the clinician. The electronic medical record will allow for a longitudinal

source of information regarding patient history, previous encounter history, drug allergies, and other

relevant information. The development and use of such a system will allow for a significant decrease in

medical errors. The National Academy of Sciences’ Institute of Medicine estimates that the deaths

caused by medical mistakes are greater than deaths caused by AIDS, breast cancer or car accidents. The

number of deaths caused by medical error has been estimated to be 98,000 individuals per year [3].

Through effective data entry and use of the electronic medical record, the amount of deaths caused by

lack of data or erroneous data entry will be significantly reduced.

Furthermore, the development of an effective clinical information system should provide the

clinician process support, or the ability to share information. Through the creation of a standard

electronic medical record, numerous users involved in the care delivery process can utilize the

information stored on the record[1]. This accessibility will allow for all users, nurses, administrative

staff, laboratory staff, pharmacists, and physicians to update and extract information for their specific

usage needs.

A significant contributor to the creation of a universal electronic medical record is the

development of SNOMED, or the Systemized Nomenclature of Medicine, and Arden Syntax[2]. SNOMED

allows for a consistent compilation of clinical information that allows various specialists, researchers,

and even patients to share their knowledge based on a common linguistic description of health. This

knowledge sharing can be conducted across care sites and differing computer systems. Furthermore,

privacy is maintained through the use of authorization privileges. This expandable nomenclature system

can be customized for individual clinicians or nation-wide health care delivery organizations [4]. Arden

Syntax is a medical language system that is specifically designed for medical logic systems, or medical

logic modules. Each medical logic module possesses ample information to create a single medical

decision based on information entered. The use of medical logic modules allows for the constant

maintenance of patient status and clinician alert of any changes in the status of the patient [5].

In addition, the development of the clinical information system arose due to a need to maintain

clinical management control. This management of clinical practice is applicable to individual patient

management, intra-practice management, and inter-practice management. On an individual patient

basis, the measurement of quality of care, the monitoring of care provided, and feedback regarding the

care that has been provided allows for improved quality care provision. The clinical information system

would allow for the maintenance of a standard practice pattern, which includes the provision of care

based on specified care paths and/or flow sheets. Furthermore, the treatments and protocols chosen

for care provision can be compared to an industry standard, or a “best practice” methodology.

This clinical management support also applies to inter-practice management situations. By

standardizing “best practice” care methodologies; the clinician has a database against which their care

path decision may be measured. Also, the integration of the numerous care contributors into a single

information system will allow for cost management and quality control across care sites. Such inter-site

care monitoring is especially significant for large care delivery organizations such as Kasier-Permanante,

where cost control, patient health, practice pattern variation, and quality control is integral to the

business operations.

            In terms of competition support, the development of an effective clinical information

system will place health care organizations in an advantageous position relative to other

competitors.  The benefits of a clinical information system are evident in terms of the reduction

of medical error, the standardization of medical protocol, knowledge sharing, cost control,

quality control, and decision support.  Therefore, firms that are able to develop facilities in these

areas will have a competitive edge over other care delivery organizations.  Furthermore, a

clinical information system will allow for the comparison of numerical data from numerous

fields with the data obtained from other firms in the industry.  Such comparisons will allow

delivery organizations to increase the quality of care accordingly, learn from other firms, adjust

price according to competitor levels, and adjust to community demand levels.

However, the domain within which the clinical information system may provide the greatest

support is within the clinical decision support domain. Through the development of the electronic

medical record and the increased SNOMED usage, clinicians will be able to share knowledge more

rapidly on an interactive level. Furthermore, the clinical environment requires an extensive knowledge

of rare complications and revolutionary research that could dramatically alter the diagnosis of diseases

in patients. However, the breadth of information and the specialization of practice prevent physicians

from understanding or even learning that such findings may exist. Through the collaboration

technologies that would be available within a clinical information system, clinicians may be able to

rapidly contact “experts” within a specific field who would be better able to assess the patient situation

through a brief analysis of the patient’s electronic medical record and other information as provided by

the physician.

Currently, the adaptation of new technology within the health care sector is fairly slow, with

change hinging more on cultural features of the organization or entity than on the technological benefits

that are produced by the implemented system. Therefore, the clinical information system technology

has become increasingly demand driven, in that the usage of the systems will only increase as long as

there is an acceptance of the new technology within the environment by the physicians. In order to

ensure successful integration, the organization must ensure that the key players, mainly the physicians,

are informed of the benefits to the individual physician, the health care organization, and health care as

a whole [6].

 

MD benefits of the Clinical Information System

            The primary benefit of the clinical information system will be within the domains of task support

and decision support[3]. The task support field has been revolutionalized through the implementation

of the electronic medical record in conjunction with SNOMED. The universalization of the electronic

medical record will increase the accessibility of patient information to clinicians as well as increase the

amount of data available for clinical use, reducing medical error significantly.

However, the greatest tool to increase the standardization of care, reduction of practice pattern

variation, successful and effective diagnosis, and correct care path choice will result from the

development of the clinical decision support domain of the clinical information system. Clinical decision

support software offers the possibility to improve the quality and reduce the cost of care by influencing

medical decisions at the time and place that these decisions are made.

An ideal clinical information system would alert physicians when outlier results are returned from

data entry of laboratory testing. The data attained for a specific patient can then be compared to the

general population to indicate whether the data is within the normal fit or is an outlier that may require

further analysis. Such a practice would induce the physician to notice certain data that may otherwise

go unnoticed, and therefore, alter the diagnosis of the patient. Also, the physician may interact with the

system on a hypothesis-testing basis. A physician may enter a possible diagnosis into the system and

then receive feedback from the system regarding the plausibility of such a diagnosis being true. This

allows physicians to receive guided feedback during their consideration of similar diagnoses, which may

be significantly different based on their appropriate care path.

The development of an effective clinical decision support system will have a significant impact on

practice methodology. Clinical decision support systems are intended to receive patient data and utilize

that data to propose a series of possible diagnoses and a course of action. The advent of such a system

will provide physicians with a guideline through which the physician can model their decision.

Furthermore, the clinical decision support system can lead to a reduction of the practice pattern

variation that plagues the health care delivery process. By reducing practice pattern variation, the

overall cost of healthcare may be reduced. Also, an effective clinical decision support system can

recognize drug-drug interaction and patient complications that would otherwise be unrecognized by the

physician to provide a valid, efficient, and “best practice” solution to the patient diagnosis process.

The dynamic environment surrounding patient diagnosing complicates the process of diagnosis.

Numerous significant input variables, special patient circumstances, and the basic complexity involved in

the diagnosis process limits the accuracy of a given clinical decision support system. However, the

potential advantages that are introduced through a successful system are significant. Primarily, possible

diagnoses could save numerous patient lives, since an information system may be better able to

synthesize vast amounts of information. Furthermore, the diagnosis suggestion may allow for better

standardization of care delivery and the care path followed.

Ever since computers were first coming into use, it was believed that computers could model

the clinical problem solving techniques used by physicians. As early as 1970, William Schwartz of Tufts

University School of Medicine wrote: “Computing science will probably exert its major effects by

augmenting and, in some cases, largely replacing the intellectual functions of the physician.” [7]

However, the dynamic, complex, and unique health care environment has hindered the

development of a standard clinical decision support system that would enable the universalization of

any clinical decision support system. Several factors including lack of investment, lack of leadership

from practicing physicians, medical schools are responsible for the dreams of an ideal clinical decision

support from being realized.

 

DEVELOPMENT OF DECISION SUPPORT SYSTEM DOMAIN

            Medical diagnosis is a complex human process that is difficult to represent in an

algorithmic model.  Not only does medical diagnosing require the understanding of symptoms,

drug-drug interactions, and patient history, the diagnosing process requires knowledge of

diseases in general as well as the general population.  Furthermore, the system would have to be

updateable to constant changes that accompany the scientific development that is a result of the

extensive research within the medical field.  Also, the system would have to be able to utilize

varying levels of data in order to diagnose an individual.  While one patient may have data

showing high cholesterol, chest pain, higher blood pressure within an arterial section, and

previous heart attack history within the family, another patient may only show high cholesterol

and chest pain.  While both patients may require a catheterization, the limited data of the second

patient may limit the ability of the diagnosis, and therefore, could lead to the misdiagnosis of the

patient.

            Furthermore, it is imperative that the diagnosing systems provide reasoning for the

medical diagnosis provided.  Such a process would allow the physician to understand the reasons

the system may have had for a specific decision that may have been made.

            Clinical information systems have developed from rudimentary data entry and retrieval

on an intra-hospital basis, to real-time data retrieval, multi-user data entry, multi-access data

retrieval, knowledge sharing, sophisticated consultation, patient and inter-practice management,

competition support, and enhanced decision support functionality.  With the advent of the

physician workstation, hand-held data entry systems[4], voice recognition systems, and real-time

clinical data retrieval and electronic medical record update, the clinical information system is

becoming a comprehensive system integrating many aspects of the care delivery process. 

Innovative point-of-care support, such as vital sign monitoring, medication administration

monitoring, basic chart maintenance, lab and drug orders administration, and alerting, are

reducing labor needs while increasing accuracy and quality through the continuous update of the

electron medical record.  The development of technology that has led to greater “alerting and

protocol support, utilization control, case management, outcome management, and executive

decision support” [8], have enhanced the care delivery process, particularly the decision support

aspect of the clinical information system.

            Clinical decision support systems have evolved from a foundation based upon statistical

algorithms to complex artificial neural networks.  The early decision support systems, also

termed medical diagnostic decision systems, were based on Bayesian statistical theory[5],

providing crude probability diagnoses based on certain critical variables [9].  In 1994 Berner et al

published the results of a study in which four commercially available medical diagnostic systems

were challenged to diagnose a series of 105 patients each of whom had been referred to a

consultant and in which of whom a diagnosis had been established.  The programs studied

included Dxplain, Iliad, Meditel and QMR.  The proportion of correct diagnosis ranged from

52% to 71% and the relevant diagnoses ranged from 19% to 37%.  Thus it appeared that

computer aided diagnostic systems had failed to deliver on their promise [10].

The complexity of the health care environment requires significant adaptations in order to

maintain legitimate diagnoses. Clinicians do not combine clinical data using probability used by the

computer but use case specific knowledge and heuristics based on their experience. Hence earlier

systems that attempted to replace the clinician were largely unsuccessful. Ralph Engle, one of the

pioneers of computer assisted diagnosis wrote: “ Our experience confirms the great difficulty and even

impossibility, of incorporating the complexity of the human thought into a system that can be handled

by a computer. We concluded that we should stop trying to make a computer act like a diagnostician

and concentrate instead on ways of making computer-generated relevant information available to

physicians as they make decisions.” [11]

 

Current Practices in Decision Support

Systems

Previous decision support systems have utilized the Arden syntax in conjunction with

HL7 standards and medical logic modules to create decision systems ranging from single

decision models to complex, sequenced decision models.  Each medical logic module contains,

within itself, the ability to make one single medical decision based on data entry.  However,

through the sequencing of various medical logic modules, fairly complex models may be

created.  Numerous clinical information system vendors have developed such decision support

systems, including; HBOC, IBM, Siemens Medical Systems, and Health VISION.  These

systems have been implemented within numerous care delivery sites, including; Columbia-

Presbyterian Medical Center, JFK Medical Center, Ohio State University, and Meridian Health

Systems.

 

Recent Shifts in CDSS:

In recent years, clinical decision support systems have begun utilizing artificial neural networks.

However, the shift to the artificial neural network continues to utilize the Bayesian statistical theory, but

is able to integrate greater decision variable than previously possible. Previously, medical diagnosis

systems were effective in evaluating outcomes for a group of patients, yet failed to perform as

successfully on a patient-by-patient basis. Through the application of the artificial neural network

system, this success rate on an individual patient basis may be increased significantly. The neural

network is able to accept numerous input variables, including demographic information, admission

information, previous diagnosis information, and patient history to rapidly create possible diagnoses

[12]. Rather than provide a single diagnosis for a specific patient, the system returns a set of possible

diagnoses from which the clinician may choose based on their own discretion. Advanced systems are

able to return probability estimates of the likeliness of a particular diagnosis. Furthermore, these

advanced systems are able to propose “best practice” methodologies for care delivery or flow sheets

that dictate action steps in the care delivery process.

However, in order to effectively determine “best practice” methodologies, clinical practice

guidelines must be set. Clinical practice guidelines that define what steps are necessary in order to

ensure quality care provision can be separated into decisions, actions, and processes. The decision

model includes selection of which variables to consider and at what weights, selection of diagnosis, and

consideration of alternative diagnoses. By utilizing such a flexible system, the patient and the physician

become the “chooser” in the environment, being able to control what information is relevant and which

result to act upon. Furthermore, the action model specifies which actions need to be performed. These

actions include the specification of type of action and temporal limitation (i.e. take dose for 3 months)

through the standardized medical terminology available. Finally, the process model organizes actions

sequentially and hierarchically in order to determine which actions are crucial to the care process and in

what order the care should be delivered [13]. The creation of clinical practice guidelines is necessary in

order to have a template with which the system may prescribe diagnoses, actions, and processes.

 

Regulation

With the increasing number of vendors producing such systems there is increasing

variability in their quality.  This is a cause for concern as a simple mistake in a clinical decision

support can lead to the loss of a life. Hence the need for enhanced oversight and regulation is

needed.  The FDA has regulated that CDSS’s are similar to medical devices. However the legal

responsibility for the treatment and advice given to the patient will rest with the clinician

regardless of whether he was assisted by the CDSS [14].

XML

XML is a type of computer language that stands for eXtensible Markup Language. It is

considered to be the future of computer applications in health care. Its acceptance and widespread use

as a method for defining clinical content in text documents depends on the establishment of standard

vocabularies so that healthcare organizations can exchange electronic information with one another.

The design of XML is to highlight the content of information rather than its appearance, which is the

case with HTML (Hyper Text Markup Language). This technology allows uses to create queries and

defined structures of information based on relevant content. This means that health care providers and

look at a chart, discharge summary, or other hospital records and isolate only the information they

need. Users can search for information from a number of sources and bring it together in one place.

They can sort the relevant information and group it by a wide array of classification systems as befits

their needs. This data can then be extracted to fit any type of order, form, or document that the user

requires.

XML can also be used to create documents that pursue set pathways according to criteria such

as content, intent, and origin. One example of this potential is having a document system that requires

an attending physician’s signature. Any chart signed by a resident without the attending’s signature

would be routed back to the attending physician for review. Thus, this technology creates efficiency

and speed by creating a system that minimizes errors and checks for complete and full workup in clinical

documentation.

On the technical side, XML files can typically be used in conjunction with HTML and with other

computer systems. This flexibility will allow users to uniformly obtain all the information they need

from a wide range of sources in order to filter out what information they precisely need. Hence, XML

appears to be the binding force that culminates clinical data from multiple systems and presents it in a

form that is easy to filter and utilize [15].

Increasing Acceptance

The trend for the future shows increased dependence on clinical decision support systems by

physicians.  Constant contact with such systems will ensure that the most optimal level of care is

provided utilizing both physician judgment and technological innovativeness.  Such a future will

mean that physicians and other health care providers will have to change the way they collect,

sort, and use health care data.  There are many benefits to such systems as well as barriers to

use.  It is important to look at such barriers in order to understand how to increase support and

acceptance to ensure successful implementation.  It is only a matter of time before these systems

are common place in all health settings.

 

The Role of Physicians and Barriers to Use

It is extremely important for physicians, caregivers, staff, administrators, and technical experts

to work in collaboration in the design, implementation, and improvement of decision support systems.

The physician role has expanded significantly in today’s managed care environment. Physicians and

other clinicians need to be involved in throughout the whole process of finding, designing,

implementing, and improving such a system. Their input and support is critical because of the significant

role that physicians play in the health care delivery process [16].

The acceptance of these systems has not been as easy as most other healthcare technologies.

One reason for this is the fact that these systems including decision support systems affect the long

history of traditional medical practices. New systems are changing the ways physicians think and

behave [17]. Failure to accept these systems among physicians occurs when implementation does not

provide direct benefits to their users and the process of implementation itself changes the traditional

practices of the clinical environment [18].

Organizational Environment and Barriers to Use

The culture in a health care setting can be very complex. This is because there are a many types

of people involved in the system. Not only are there patients and physicians, but there is also the

administration, technical experts, and other staff that add to the web of issues, tensions, and interests

that exist. There are many values and mode of practice that exist in such a web that often complicate

the culture of the health care environment as a whole. It is natural to conclude that with such a large

group of diverse people, there are often a difference of values and interests. Therefore, adopting a

major acquisition such as a clinical decision support system requires that there is overall acceptance and

support from all relevant parties to ensure successful implementation. The benefits and how people

evaluate them with regards to how they fit within their practices are the key factors indicative of success

[19].

Despite evidence that clinical information systems can improve patient care, there have been a

number of instances where implementation has not been successful [20]. Barriers here are due to

organizational stigmas that the systems are not noticeable benefits to the practice of care. Others argue

the cost benefits of certain systems within their operating margins. Like physicians, those medical staff

and administrators who are not properly acquainted with new systems argue that they negatively affect

care in areas of patient waiting times and work load. However, negative results in these areas occur

when those using the system do not have adequate training and support for such a system whereby the

use of it turns out to be negative rather than positive. [21, 22, 23]

Since the acceptance of decision support systems depends heavily on physicians, it’s the values

that they hold which determine if they view new systems as beneficial. These values concern the

patient-physician relationship, the quality of patient care, the balance between clinical guidelines and

decision support technology, and physician autonomy.

Quality of Treatment

Physicians’ attitudes about what a piece of technology can do has more of an effect on

acceptance of that technology than what it can do in a realistic setting. This belief is confined towards

the physician’s own practices and how they operate. If there is a direct benefit here, then a system has

improved the quality of care.

Clinical Procedures and Decision Support Technology

The balance between the science of medicine and the art of it is a pressing issue that will

influence acceptance.  There is natural tension between the capabilities of technology and the

method of medical practices that have existed for years.  Physicians struggle over whether to let

technology based on population characteristics guide them in their decision making as opposed

to making decisions based on their own experience and the novelty of each patient’s case.  The

advent of providing accurate information through clinical information has been essential in the

improvement of patient care.  However, just because information is provided does not mean that

it is used.  Physicians may ignore information offered up by some clinical decision support

systems.  Those who do use it may not use it to its fully capability or may only use it for a short

time before discontinuing its use.  Physicians are worried about the legal ramifications of not

following the advice of decision support systems.  However, the issue that decision support

systems are only a tool for advice and not a computer making the final decision has increased

physicians’ comfort level with decision support systems [21].

 

Relationship between Patient and Physician

The relationship between a physician and his/her patient is crucial to quality care. The

acceptance of decision support systems depends on their ability to cater to this need. Systems have to

make the physician feel more equipped to provide better care while instilling trust and confidence in the

patient that the technology is not a replacement of the physician, but a tool that enhances the

treatment process.

 

Physician Autonomy

Medicine is characterized by a simple, but long lasting philosophy. The ability to treat patients

requires not only individual expertise, but experience in being able to effectively address the needs of

patients. Therefore, physicians have favored their own clinical judgment in determining the needs of

particular patients over broad policies, administrative guidelines, and the reliance on technology.

Therefore, promoting awareness about decision support systems capabilities has to be done in a way to

break down social barriers and stigma that are associated with physicians’ egos [24].

CONCLUSIONS

Current trends in the United States will not only lead to increased spending in the health care

arena but also an accelerated growth of the acceptance and spending of health care information

technology. The necessity for innovative and dependable clinical information systems with decision

support capabilities is crucial. Increasing systems’ acceptance depends on the culture of the hospital as

well as the involvement of physicians. The involvement of all health care professionals especially

physicians in the selection and implementation of the system from the outset is essential. Not only will

this ensure support, but as a result the frequency of communication is likely to increase amongst one

another. In turn, this frequent consultation and communication is likely to better the quality of patient

care as well as the patient-physician relationship [25].

Secondly, it is essential to consider in advance the new system’s effects on the culture, practices,

and attitudes of the people in the organization. Explicitly identifying how people and groups in the

organization will benefit specifically will lend support for its implementation. The use of information

systems by physicians will occur if that system will allow them to provide better care for their patients.

Benefits to the organization, in general, will not be as successful to motivate or even inspire physicians

to alter the way they have practiced medicine.

Finally, healthcare organizations must anticipate and be prepared to handle a diverse array of

changes that will occur during the implementation process itself. Therefore the organization needs to be

able to operate normally on schedule while operating the implementation process as well. By phasing in

the system's implementation and anticipating problems proactively, the hospital should be able to

reduce the number of negative experiences associated with the introduction of a new system [26].

References:

 

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(http://www.cwru.edu/med/epidbio/mphp439/Clinical_Decision.htm)-Notes

Computerised decision support systems in order communication for diagnostic, screening or monitoring test ordering: systematic reviews of the effects and cost-effectiveness of systems.

Top tips to choose the right bank for your student loan (TopTipsNews)Main C1, Moxham T, Wyatt JC, Kay J, Anderson R, Stein K.

Author information

Abstract

BACKGROUND:

Order communication systems (OCS) are computer applications used to enter diagnostic and therapeutic patient care orders and to view test results. Many potential benefits of OCS have been identified including improvements in clinician ordering patterns, optimisation of clinical time, and aiding communication processes between clinicians and different departments. Many OCS now include computerised decision support systems (CDSS), which are information systems designed to improve clinical decision-making. CDSS match individual patient characteristics to a computerised knowledge base, and software algorithms generate patient-specific recommendations.

OBJECTIVES:

To investigate which CDSS in OCS are in use within the UK and the impact of CDSS in OCS for diagnostic, screening or monitoring test ordering compared to OCS without CDSS. To determine what features of CDSS are associated with clinician or patient acceptance of CDSS in OCS and what is known about the cost-effectiveness of CDSS in diagnostic, screening or monitoring test OCS compared to OCS without CDSS.

DATA SOURCES:

A generic search to identify potentially relevant studies for inclusion was conducted using MEDLINE, EMBASE, Cochrane Controlled Trials Register (CCTR), CINAHL (Cumulative Index to Nursing and Allied Health Literature), DARE (Database of Abstracts of Reviews of

Effects), Health Technology Assessment (HTA) database, IEEE (Institute of Electrical and Electronic Engineers) Xplore digital library, NHS Economic Evaluation Database (NHS EED) and EconLit, searched between 1974 and 2009 with a total of 22,109 titles and abstracts screened for inclusion.

REVIEW METHODS:

CDSS for diagnostic, screening and monitoring test ordering OCS in use in the UK were identified through contact with the 24 manufacturers/suppliers currently contracted by the National Project for Information Technology (NpfIT) to provide either national or specialist decision support. A generic search to identify potentially relevant studies for inclusion in the review was conducted on a range of medical, social science and economic databases. The review was undertaken using standard systematic review methods, with studies being screened for inclusion, data extracted and quality assessed by two reviewers. Results were broadly grouped according to the type of CDSS intervention and study design where possible. These were then combined using a narrative synthesis with relevant quantitative results tabulated.

RESULTS:

Results of the studies included in review were highly mixed and equivocal, often both within and between studies, but broadly showed a beneficial impact of the use of CDSS in conjunction with OCS over and above OCS alone. Overall, if the findings of both primary and secondary outcomes are taken into account, then CDSS significantly improved practitioner performance in 15 out of 24 studies (62.5%). Only two studies covered the cost-effectiveness of CDSS: a Dutch study reported a mean cost decrease of 3% for blood tests orders (639 euros) in each of the intervention clinics compared with a 2% (208 euros) increase in control clinics in test costs; and a Spanish study reported a significant increase in the cost of laboratory tests from 41.8 euros per patient per annum to 47.2 euros after implementation of the system.

LIMITATIONS:

The response rate from the survey of manufacturers and suppliers was extremely low at only 17% and much of the feedback was classified as being commercial-in-confidence (CIC). No studies were identified which assessed the features of CDSS that are associated with clinician or patient acceptance of CDSS in OCS in the test ordering process and only limited data was available on the cost-effectiveness of CDSS plus OCS compared with OCS alone and the findings highly specific. Although CDSS appears to have a potentially small positive impact on diagnostic, screening or monitoring test ordering, the majority of studies come from a limited number of institutions in the USA.

CONCLUSIONS:

If the findings of both primary and secondary outcomes are taken into account then CDSS showed a statistically significant benefit on either process or practitioner performance outcomes in nearly two-thirds of the studies. Furthermore, in four studies that assessed adverse effects of either test cancellation or delay, no significant detrimental effects in terms of additional

utilisation of health-care resources or adverse events were observed. We believe the key current need is for a well designed and comprehensive survey, and on the basis of the results of this potentially for evaluation studies in the form of cluster randomised controlled trials or randomised controlled trials which incorporate process, and patient outcomes, as well as full economic evaluations alongside the trials to assess the impact of CDSS in conjunction with OCS versus OCS alone for diagnostic, screening or monitoring test ordering in the NHS. The economic evaluation should incorporate the full costs of potentially developing, testing, and installing the system, including staff training costs.

http://www.ncbi.nlm.nih.gov/pubmed/21034668