Parallel Session 1.9 Using Health Analytics for Improved Outcomes

© 2011 IBM Corporation

Using Health Analytics to Improve Outcomes

Jaap Vink – IBM Worldwide Predictive Analytics Leader Public Sector

21 June 2012

© 2011 IBM Corporation2

Shortage of resources, forcing greater workforce

productivity and efficiency

New players with revolutionary technologies and treatments for health and care

Incidence and cost of chronic and

reemerging infectious diseases

Shift from local to national and global

contexts

Changing demographics and lifestyles

Empowered consumers expecting better value, quality and outcomes

Marketplace forces require a new approach for the healthcare industry


How can we get from this…

PatientDoctor


…to this?

Patient

Payers

Providers

Doctors

Government

Family

Friends

Community

Patient

© 2011 IBM Corporation5Source: Brian B. Spear, Margo Heath-Chiozzi, Jeffery Huff, “Clinical Trends in Molecular Medicine,” Volume 7, Issue 5, May 1 2001,

Pages 201-204.


Ask yourself…

What is your disease management program?

How do you determine specific treatments for individual patients?

How much of your treatment plan decisions are based on data?

How often are educated guesses made based on your experience?

Do you truly understand your patients’ attitudes and sentiments?

How costly is it if a treatment plan is not effective?

Are you able to predict treatment effectiveness?

Can you quantify cost of treatment plans to benefit of patient satisfaction?

How do you predict which holistic treatment plan is the right one for each

patient?


Evolutionary Solutions for Health Analytics

Foundational

Innovative

Differentiating

BreakawayInsight for Healthcare Providers andDecision Makers

The Next Best Action


Strategic Tactical Operational

Years Months Weeks Days Hours Now

Improve policy makers’ decisions with

Forecasts and Optimization

Help individual contributors take the

Next Best Action

Define the Strategy Run the Business

Time to Business Impact

Improve senior management visibility with

Key Performance Predictors

How IBM Business Analytics Provides ValueAligning Strategy with Operations: Insights and Action


The Predictive Advantage

“NOW”

Traditional BI and Conventional Analysis:• Insight, metrics, etc. up to this point in time• User initiative to explore aggregate data

Predictive Analytics:• Algorithms automatically discover significant patterns• Deliver deep insights to improve strategic and

operational decision making• “Learn” from historical data – create predictive models

“NOW”

“NOW”

Deploying Predictive Models• Leverage current and historical data• Make robust predictions on current and future cases• Embed in business processes to transform

decision making and drive better outcomes

M

KPI

KPIKPI

Sense & Respond

Predict & Act

How can we improve the care and prognosis for patients diagnosed with rare diseases?

“The improvement in the quick diagnosis and treatment of rare diseases can mean the difference in the lives of patients.”

– Geert Smits

One of the biggest challenges in treating rare diseases (those that affect fewer than five people in 10,000) is the provision of an

early diagnosis. Because of the rarity of their disease, patients may not be diagnosed early enough for the most effective treatments. UZA wanted to be able to diagnose and treat these rare diseases earlier and more effectively. To do so, it needed to be able to access and use information from many sources.

The Opportunity

Universitair Ziekenhuis Antwerpen (UZA) - (University Hospital of Antwerp)

What Makes it SmarterThe University’s rare disease diagnosis platform allows for an earlier, quicker and more accurate diagnosis by integrating both medical expertise and data mining tools. Rules are generated much faster and more accurately through a predictive model based on known patient data. In comparison with a pure rule based system, a combination with data mining tools provides both higher sensitivity and more specificity. The solution can serve as an intelligent and dynamic knowledgebase on rare diseases. The improvement in the quick diagnosis and treatment of rare diseases can mean the difference in the lives of patients.

Real Business Results• Rules are generated more quickly and accurately using a predictive model based

on known patient data

• The solution can serve as an intelligent and dynamic knowledgebase on rare diseases, improving the quick diagnosis and treatment of rare diseases

• Compared with a pure rule based system, a combination of rules and data mining tools provides both higher sensitivity and more specificity

For this University Hospital, a rare-disease diagnosis platform allows for an earlier, quicker and more accurate diagnosis by integrating both medical expertise and data mining tools

Healthcare | BAO | Netherlands

What if you could know in advance which patients would benefit most from initial interventions?

“In our opinion we were provided with enough support and it met our needs to incorporate this solution into the work we were beginning.”

– Dr. Luis Beato Fernández, Ciudad Real Hospital

The hospital wanted to identify positive and negative prognosis factors in long-term monitoring of patients being treated for serious eating disorders, such as anorexia and bulimia. Because such disorders affect almost 3% of Spain's population, the goal was an urgent one. But due to the high number of variables that potentially factor into prognosis, the hospital had been unable to execute the complex statistical analysis required to identify those that were most important. A more powerful solution was needed.

The Opportunity

Ciudad Real Hospital

What Makes It Smarter The ability to effectively handle and analyze data is essential to diagnosing illnesses earlier and speeding patients to recovery. Ciudad Real Hospital implemented a powerful

predictive analytics solution that enabled its practitioners to establish reliable forecasting, control and early diagnosis variables for patients with severe eating disorders. The solution provides more accurate initial patient evaluations, and has helped the clinical staff identify specific subgroups within the total patient population for whom initial interventions should lead to more successful treatment outcomes. The solution is also pointing the way towards new lines of research. For example, in supporting studies on the link between patient motivation and treatment effectiveness, the solution has discovered a direct link between patients' expectations (e.g., feelings of despair) and poorer outcomes, even when other variables previously identified as being able to predict treatment responses were controlled.

Real Business Results• Enabled 100% improvement in data handling for more accurate initial patient evaluations

helping to develop more successful treatment outcomes

• Uncovered specific links between patients’ expectations and treatment results

• Helped identify new lines of research to be explored

• An estimated 5-10% improvement of efficiency and effectiveness treatments of several chronic diseases

A Spanish hospital is using predictive analytics to make significant improvements in the treatment of severe eating disorders.

Healthcare | Business Analytics & Optimization | Southern Europe

What if you could find patients at high risk for serious disease by looking at analytic data?

Like many large research hospitals, Hospital Santa Bárbara in Spain has amassed a large amount of research data and a wealth of information on past and present patients.

However, the hospital was often at a loss in terms of how to use that data to improve processes and outcomes. Researchers and hospital staff wanted to be able to extract critical data from various sources and analyze it to better diagnose ailments, refine treatments and innovate with new devices and procedures.

The Opportunity

Hospital Santa Bárbara

What Makes it SmarterSometimes leg pain is just that. Sometimes it’s deep-vein thrombosis (DVT), a blood-clotting condition that may not be discovered until clots reach the lungs—often with fatal results. But researchers at Hospital Santa Bárbara have been able to use statistics software to extract research data as well as patient records, and analyze that data to more effectively target which patients are at risk for chronic, hard-to-detect diseases. By using statistical analysis, researchers came up with a more effective diagnostic model for DVT, pinpointed that 44 percent of colon cancer patients were between 75 and 79 years of age, and determined that chronic obstructive pulmonary disease patients with a BODE rate of greater than 7 had an 80 percent mortality rate within 48 months after diagnosis. With insight from in-depth analysis, this hospital and others like it can more effectively screen patients for these serious conditions and others, advise preventive measures before disease takes hold, and even create new devices and treatments based on both new research and past experience.Real Business Results• Established a new, reliable diagnostic model for DVT, expected to enable

earlier diagnosis and treatment in high-risk patients

• Helped researchers determine that age is the biggest risk factor in colon cancer patients, enabling staff to more effectively target tests to more high-risk patients

• Reduced the cost of colon cancer diagnosis by 99 percent with targeted testing

• Enabled researchers to isolate obesity as a key risk factor for chronic obstructive pulmonary disease, helping doctors get patients on the track to good health early

A hospital in Spain uses statistics and data analysis to identify key risk factors, improve diagnosis and treatment, use resources more efficiently and effectively, and give patients a better quality of life.

Healthcare | Information and Analytics | Europe

“Based on diagnostic models for chronic illnesses, we can provide clear evidence of risk factors and prescribe more effective treatment for individuals, resulting in better health outcomes.”

What if your patient could show you what pain looks like?

Measuring our own patient outcomes gives our future patients more realistic expectations of the treatments, and by benchmarking ourselves, we can continually improve upon patient treatment options and care.

Medical offices have gotten by with paper-based patient information systems for years. Done well, they can be quite efficient, but a manual system will never match the speed and accuracy of an online data collection process. One chronic pain clinic in Australia recognized that its paper-based patient data process was taking too long, both to input data and to find that data when doctors needed it—and when patients are in pain, even a few minutes is too long. The clinic wanted a more efficient system, but also wanted more analytical power to take that patient data and analyze it for more insight into diagnosis and treatment.

The Opportunity

Metro Spinal Clinic

What Makes it SmarterWhat causes chronic pain? Sometimes it’s obvious, but sometimes getting to the root of pain takes a little more digging and a lot of hindsight and research. One chronic pain management clinic in Australia is diagnosing and treating pain with a new solution based on online data collection and statistical analysis. Instead of filling out two-dimensional paper questionnaires, patients complete an online survey where they can describe their pain on a graphical representation of the human body. This and other information gives physicians a more visual look at patient pain. When combined with historical case data and peer discussions of pain management, staff can more accurately diagnose pain, refer treatment and raise red flags when something isn’t right. Current patients benefit from fast treatment, and future patients can benefit from the ever-growing database of information and analysis. The new system alleviates pain for the clinic as well, saving thousands of dollars in administrative costs and reducing staff labor.

Real Business Results• Reduced total administrative costs at the clinic by 75 percent• Cut the cost per survey from USD10.65 to USD1.14, a 90 percent decrease

• Increased post-treatment questionnaire follow-up rates to 85 to 100 percent

• Enabled physicians to diagnose and treat pain more quickly and accurately with real-time access to data and visual representations of patient pain

Metro Spinal Clinic, a pain management clinic in Australia implements an online patient data collection system that enables patients to describe their pain symptoms more graphically and allows faster, more accurate diagnosis and treatment with statistical analysis.

Healthcare | Information and Analytics | Australia

A Large Japanese Hospital What if predictive analytics could treat liver disease?A Japanese hospital uses regression and decision-tree analyses of patient records to predict the effectiveness of specific treatments for each individual patient.

Healthcare | Information & Analytics | Japan

The OpportunityDetermining why one treatment works for one patient’s liver disease and not another’s is greatly enhanced by building predictive models based on more than 400 factors, such as age, sex, race, blood type, blood sugar content, body build, medical history and lifestyle. The models enable the hospital to more accurately assess the percentage of cure rates for specific treatments.

The SolutionThe solution captures detailed patient records and aggregates them into a central database, which provides a wealth of data in which to run decision-tree and regression analyses. Through the creation of predictive models, based on the records of patients who have had liver disease, doctors are able to determine which treatment options would be the most effective for each individual patient.

What Makes it SmarterBy analyzing more than 400 factors per patient and by cross-functionally aggregating that data, doctors are able to identify the specific treatments that will yield the best result for each for patient.

InsightSmarter healthcare is using predictive analysis to help fight infectious disease and improve individual patient care.

Real Business Results• Improved accuracy of virus removal by

approximately 43 percent• Enables patients to avoid unnecessary, expensive

and painful treatments if they are deemed inappropriate by the model

What if the doctor’s job were already halfway done by the time a heart attack patient checks into the hospital?

We now have a world-class analytics platform that matches our world-class reputation as a research organization. We will continue to use SPSS to support our research and hope to make further breakthroughs that enhance patient care and improve outcomes across the whole spectrum of cardiology.

In medical research, behind every discovery is a mountain of data. And much of this data is complex; any number of variables can be a factor in medical treatment. No case is the same, and every case must be considered. One Dutch research organization doing work on prehospital treatment for heart attacks had thousands of doctor, hospital and patient surveys to consider as well as treatment outcomes and ambulance records. To make sense of this data and help create a protocol for prehospital treatment, the firm needed a way to perform advanced analytics on complex human data, including predictive analytics and in-depth regression analysis.

The Opportunity

A cardiac medical research organization

What Makes it SmarterThe critical window for treating a heart attack is within one to two hours after the initial attack occurs. After that, patients require more invasive treatment and longer recovery, and the heart may sustain more damage in the long run. That often means administering treatment before a doctor even enters the picture. How does a paramedic recognize a heart attack and know treatment options? Using advanced statistical analysis and predictive analytics software, one medical research company created an algorithm that tells paramedics the probability that a patient is having a heart attack based on symptoms, patient history and other factors. The program will then suggest the most appropriate prehospital treatment and also direct the ambulance to the nearest hospital with full cardiac care facilities. The solution saves precious time and money and helps ensure that hearts keep beating strong for years to come.

Real Business Results• Expects to improve patient survival rates and recovery times by treating heart

attacks sooner after onset

• Stands to improve treatment accuracy with a proven methodology and algorithm for heart attack diagnosis and prehospital treatment

• Expects to save hospital operating costs and patient expenditures by reducing the average length of stay

A cardiac medical research firm in the Netherlands helps paramedics diagnose and treat heart attack patients on the way to the hospital when it applies advanced statistical analysis and predictive analytics to research data.

Healthcare | Information and Analytics | Northeast Europe

What if a cancer research center could create individualized cancer treatments that would reduce the number of unnecessary treatments while improving therapeutic outcomes?

By providing physicians with vital input on what worked best for patients with similar clinical characteristics, the institute can help improve treatment effectiveness and the final patient outcome.

The fact that one-size-fits all cancer treatment may result in more than half of all patients receiving unnecessary treatment helps bring the goal of finding the right treatment plan for each patient into sharper focus. This leading Italian cancer treatment and research center wanted to improve patient care by tailoring treatment approaches to specific individuals. The institute needed the ability to analyze past treatments and cases, and combine that information with the patient’s personal statistics and disease profile, to create a fact-based treatment plan for each patient. In addition, being able to analyze overall outcome data would help the institute provide more cost-effective, efficient care for its patients.

The Opportunity

A leading Italian cancer research institute

What Makes it SmarterUntil now, “personalized” treatments in cancer and other disease treatment have generally been based on clinical trial results, a doctor’s subjective memory of past cases, and even intuition. Finally, true evidence-based, personalized medicine is being implemented at the institute, where an in-depth analysis of a patient’s personal makeup and disease profile, combined with insight gained from the analysis of past cases and clinical guidelines, enables doctors to provide an optimal treatment plan for each patient. The solution proactively shows the physician statistics on similar clinical cases, possible alternative treatments and predicted outcomes for each, allowing the doctor to make a truly informed decision. One insight from the solution showed that, statistically, physicians tend to give more aggressive medical therapy to women who are sick as opposed to men with the same problem. Knowing this, physicians can guard against such over-treatment, ensuring that patients receive only the medicine and procedures they need. Real Business Results• Avoids unnecessary treatment (estimated to be up to 60% of all treatment) and

delays in treatment delivery

• Creates tailored and personalized treatments, increasing the chances of successful outcomes

• Improves hospital performance, both clinical and operational, by providing a “big picture” view of treatment delivery, helping streamline processes and lower costs

This Italian medical institute pioneers the use of advanced analytics to analyze insights from clinical data, combined with patient information, to create personalized treatment plans for its patients, helping it treat cancer and other diseases more effectively.

Healthcare | Business Analytics and Optimization | Italy


Hospitals are challenged to provide care amongst various obstacles

Patients are admitted in fluctuating numbers

Independently occurring injury & illness generates inconsistent work flow

24 hours a day, 7 days a week, 365 days a year

Staff is scheduled by shift, hourly, or on call

Short staff causes delays & increased patient wait times

Excess staff costs money

Medical devices are used for specialized diagnosis & treatment, in limited quantities

Occupied by another patient

In need of repair or maintenance


Approach to Smarter Hospital Operations

• Conduct surveys of patients and employees in a variety of formats• Understand first hand the source of operational concerns

Collect Information

• Use existing data such as HR information, patient records, national health databases, and equipment reports & maintenance tickets

• Management with experience in best practices in operational efficiencies and staffing contribute to the understanding of the hospital’s business goals

Incorporate feedback with hospital data & subject matter experts

• Operational inefficiencies & bottlenecks are identified with the breadth & depth of data provided

• Informed actions can be taken based on robust models developed

Analysis & predictive models provide insight


Predicting Hospital Operations

Hospitals and clinics can make predictions in regards to the number of patients likely to be admitted, the number of staff members to be scheduled in a variety of positions, and the medical devices that need to be readily available.

Predictive Analytics can better determine staff and equipment needs in order to maximize their use in the hospital, with limited resources.

Goals:– Reduce costs– Increase patient satisfaction– Have an active & efficient operations strategy – Obtain actionable insight into causes of delays– Identify greatest sources of delays– Forecast accurate staff needs

What could you do if you had up-to-date healthcare cost statistics for an entire country at your fingertips?

Achieving this level of accuracy and foresight in modeling and understanding healthcare costs is unthinkable when you’re limited to the calculations of a spreadsheet. The dynamic statistical analysis we have now enables us to do so much more when calculating DRG fees and relative weights.

As the organization responsible for calculating and recommending healthcare fees in a Central European country, this government organization must maintain unparalleled knowledge of clinical treatment paths and trends in the healthcare sector. The organization collects and analyzes enormous amounts of hospital data. Manual data collection and spreadsheet calculations, however, made it difficult to navigate the complex algorithm required to understand costs and define fees. To increase accuracy and speed, the organization needed powerful statistical analysis capabilities.

The Opportunity

A government healthcare organization in Central Europe

What Makes it SmarterAround the world, countries are working to standardize healthcare fees using diagnosis-related groups (DRGs). Though DRG codes help to create fair payment systems, they are complicated and dynamic, built on extremely complex calculations. This government organization has reined in the complexity by applying sophisticated statistical analysis to vast amounts of data collected from every hospital in the country. The organization tested multiple data models to find an algorithm that effectively mirrors the country’s healthcare system. The continuing influx of data creates a feedback loop that refines the accuracy of the algorithm over time. With these powerful analysis capabilities, the organization can calculate relative weights to account for variations in hospital costs, monitor macro patterns in treatment paths and identify true cost outliers that might signal a need for change in DRG codes. Hospitals also benefit from the insights, using the data to benchmark their costs and spot inefficiencies. For example, if a hospital sees that its costs for treating heart disease patients far exceed the norm, it can take steps to find and fix inefficiencies.

Real Business Results• Reduced the time to perform complex calculations of relative weights from 3 days

to 5 minutes—a more than 99 percent improvement

• Increased the frequency of analysis by 1,100 percent with one-sixth of the manual labor requirement

• Shifted focus from data collection and processing to in-depth statistical analysis, yielding a clearer view of the healthcare system and more accurate DRG codes

A government healthcare organization uses sophisticated statistical analysis to calculate fair prices for healthcare services under a diagnosis-related group (DRG) system while helping hospitals root out inefficiencies .

Government, Healthcare | Smarter Analytics | Central Europe

What if a tool for scientific research also could help you better manage efficiencies and improve services?

“We are now in a better position to optimize patient care across our large network – one that conducts more than 13 million medical appointments and 200,000 surgical procedures per year, as well as managing 1.2 million hospital stays annually."

SESCAM has a large number of clinical research centers with projects focusing on improving diagnosis and treatment. The organization needed appropriate statistical and analytical tools to support these projects that could, at the same time, contribute to the more effective management of all of its hospitals and health centers by detecting inefficiencies and enhancing the level of service provided to clients.

The Opportunity

SESCAM

What Makes It SmarterDetecting inefficiencies quickly across a large healthcare network is key to better resource management and service delivery – but doing so effectively entails managing, and analyzing, enormous quantities of data. Implementing a sophisticated analysis and asset management solution has vastly improved this health service's ability to manage operations across a vast web of 6,000 doctors, 31 pharmacists, 80 dentists, and 7,000 nurses. In the health sector, the solution is helping the organization easily manage all this information, identifying potential problems and inefficiencies across hospital processes and facilitating their resolution to improve quality of care. In the research field, the new solution is helping investigators in developing innovative devices to assist patients in their daily lives and to improve the cure rate of illnesses. For example, clinical researchers in the hospital's nutritional illness unit hope to improve the cure rate of individuals suffering from anorexia and bulimia, illnesses affecting an estimated 3% of the population. The solution’s predictive analytics tool has been of fundamental importance in enabling researchers to implement a long-term monitoring program of these individuals in which possible variables relating to these diseases are recorded and analyzed, with the goal of improving prognoses and treatment.

Real Business Results• Improved the efficiency of managing the organization's vast network of healthcare facilities

• Enhanced the quality of care by identifying and eliminating inefficiencies in hospital processes

• Provided researchers a powerful statistical tool for the development of more ergonomic wheelchairs for paraplegics

• Aided researchers in studying sensory capacity losses related to aging

SESCAM is using a powerful analytics and optimization solution to more effectively manage and better focus its clinical research while at the same time significantly enhancing the quality of the care it delivers to more than 2 million people.

Healthcare | Business Analytics | Western Europe

What if you could use analytics to dramatically improve patient care and quality of life?

“By quickly and accurately analyzing large volumes of data, we are helping to enhance quality of care and improve patient outcomes. The solution is making an enormous contribution, not only to us but also to the larger healthcare community.”

Having access to enormous volumes of patient data, this medical research institute wanted to find a better way to leverage all this basic information to enhance patient outcomes. A core element of the institute’s research program is the collection and analysis of large amounts of data from multiple sources and in various formats. The data need to be carefully and accurately managed and interconnected in order to create an accurate picture of patient outcomes. Without comprehensive data collection and more sophisticated data analysis, the institute was struggling to gain deeper insights across patient groups.

The Opportunity

Wesley Research Institute

What Makes It Smarter

Effective hospitals manage, integrate and analyze clinical and research data to tackle complex problems and improve patient care. This institute implemented a statistics-based analytics solution that successfully collects huge amounts of data – including patient demographics and data on discrete procedures, surgical complications, risk factors and post-operative tracking – and then quickly analyzes it to establish clinical benchmarks, identify risk factors and improve treatment results. The solution uses powerful statistical analyses to evaluate the range of factors influencing successful medical treatments. This enables the institute’s doctors to proactively modify protocols to improve quality of care: for example, doctors who are about to perform high-risk surgeries can view statistics on the risk of mortality, based on patient demographics, and make better, more well-informed decisions on behalf of their patients.

Real Business Results• Improved patient care across the hospital – for example, by identifying trauma

(bruising, bleeding and hematomas) caused by catheter use, which the hospital immediately addressed

• Improved clinician productivity by providing doctors with access to accurate data across patient groups

• Decreased time spent in the production and delivery of reports to clinicians

• Provided clinical staff with a valuable resource for ad hoc queries

Wesley Research Institute is deploying a powerful analytics tool that enables doctors to make better-informed decisions that result in improved patient outcomes.

Healthcare | Business Analytics | Australia


Reports,KPIs, KPPs

AnalysesSegments

Time series analysisProfiles

Scoring models...

Scoring

Detect & Capture

Analyze & Predict

Engage & Act

Define Thresholds

Determine the level of Risk

Define List

Assign weight (points) to each

indicator...

Domain Expertise

Controlling outcomes with predictive analytics

Demographic data

Transaction data

External data

AgeGenderAddress

...

Previous visitIssue

DiagnosisDate

...

Regional disease infoWeather infoEmployer info

...

System notificationsEmail

ReportsDashboards

...

© 2011 IBM Corporation

Thank you!!!

Questions?

Technology

Parallel Session 1.9 Using Health Analytics for Improved Outcomes