Impact of EHR on Quality of Care Farrokh Alemi, Ph.D

Impact of EHR on Quality of CareFarrokh Alemi, Ph.D.

Objectives

Review the literature Discuss the mechanism Outline future directions

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2002 2004 2006 2008 2010 2012 2014

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Use of Computerized Physician Order Entry in Hospitals

Studies cited in Health Information Technology in the United States: The Information Base for Progress

Goal

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2002 2004 2006 2008 2010 2012 2014

Year

% H

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Use of Computerized Physician Order Entry in Hospitals

Studies cited in Health Information Technology in the United States: The Information Base for Progress

Goal

In some areas,80% of Urban Hospitals

Use CPOE

Where is the beef?Why are hospitals implementing CPOE?

OperationalEfficiency

Four Eras of IT in Health Care

CQI / TQM

Efficacyof Care

PatientSafety

Patient Financial SystemsDepartmental Clinical Systems

Process IntegrationWorkflow Transformation

Data Integration: Patient-Centric ViewClinical Decision Support – CPOE

1980 1990 2000 2010 2020TODAY

ANALYTICS &CONTINUOUS IMPROVEMENT

Institute of Medicine reports

Technology Infusionfrom Other Industries

From John Cuddeback MD

Develop improved practiceDeploy improved practiceRETROSPECTIVEReal Time

InformationInformation Knowledge

DataDataData

ANALYTICALSYSTEMS

Population Level

Analytical systems are essential for integration and transformation.

Analytical models, risk adjustment Ad hoc query tools—exploratory analysis,

hypothesis generation/testing Comparative data, “best” practices Support for quality improvement teams Practice profile reports for clinicians

POINT- OF - CARESYSTEMS

Patient Level Administrative systems (scheduling, ADT) Clinical observations, assessment, plan Orders—tied to protocols, w/ decision support Tests, results, documentation of care (eMAR) Capture outcomes, key process variables Error / near-miss reporting

External DataDATA WAREHOUSES

TRANSACTION SYSTEMSCLINICAL DATA REPOSITORY

ImprovedPractice

Concept or reality?

From John Cuddeback MD

Rate of Drug Events

4031 adult admissions 11 medical and surgical units 2 tertiary care hospitals Over a 6-month period

Detected by self-report Classified as ADEs or potential ADEs

Bates et al., JAMA 1995;274:29-34

Rate of Drug Events

247 ADEs & 194 potential ADEs In 100 non-obstetrical admissions

6.5 ADEs 5.5 potential ADEs

Bates et al., JAMA 1995;274:29-34

Fatal; 1%Life

threaten-ing; 12%

Serious, 30%Significant; 57%

Rate of Drug Events

Ordering49%

Transcription11%

Dispensing14%

Administration26%

48% of errors intercepted

No errors intercepted !



Bates et al., JAMA 1995;274:29-34

Can CPOE Reduce ADE? How exactly it will do so?

Rate of Errors after CPOE

Before and after study Baseline & 3 subsequent years

One hospital All patients Three medical units Seven to ten-week periods

Bates DW, Teich JM, Lee J, Seger D, Kuperman GJ, Ma'Luf N, Boyle D, Leape L. The impact of computerized physician order entry on medication error prevention. J Am Med Inform Assoc. 1999 Jul-Aug;6(4):313-21.

Rate of Errors after CPOE

Medication errors fell 81% From 142 to 27 per 1,000 patient-days

Non-intercepted serious medication errors fell 86%


Rates of Errors after CPOE


5 months after CPOE

Allergy warning system

Drug-drug interaction system

Rates of Errors after CPOE


5 months after CPOE

Allergy warning system

Drug-drug interaction system

Just storing and retrieving data is not much of a benefit.

Analyze and use the data.

Medication Errors in an ICU

22-bed general ICU Sampled before and after

28 weeks before Hand Written Prescribing 2, 10, 25 and 37 weeks after CPOE

Unit pharmacist recorded details of errors

Shulman R, Singer M, Goldstone J, Bellingan G. Medication errors: a prospective cohort study of hand-written and computerized physician order entry in the intensive care unit. Crit Care. 2005 Oct 5;9(5):R516-21. Epub 2005 Aug 8.

Less Medication Errors

Shulman R, Singer M, Goldstone J, Bellingan G. Medication errors: a prospective cohort study of hand-written and computerized physician order entry in the intensive care unit. Crit Care. 2005 Oct 5;9(5):R516-21. Epub 2005 Aug 8.

Types of Errors HWP CPOE

Drug prescribed on incorrect drug chart section (e.g. continuous IV infusion prescribed on 'when required' part of drug chart)

2 (2.8%)

1 (0.9%)

Drug needed but not given as not prescribed properly

3 (4.2%)

5 (4.3%)

Inappropriate/inadequate additional information on prescription to adequately administer the drug appropriately

8 (11.3%

)

12 (10.3%

)

Dose/units/frequency omitted on prescription

22 (31%)

1 (0.9%)

Prescription not signed or change not signed/dated

10 (14.1%

)

39 (33.3%

)

Types of Errors HWP CPOE

Still wrong next day after pharmacist recommended appropriate correction that was agreed with doctor

0 (0%)3

(2.6%)

Dose error12

(16.9%)31

(26.5%)

Wrong drug prescribed 3 (4.2%)6

(5.1%)

Incorrect route/unit 5 (7%)8

(6.8%)Formulary not followed without reason

3 (4.2%)1

(0.9%)Administration not in accordance with prescription

3 (4.2%)3

(2.6%)Required drug not prescribed 0 (0%) 7 (6%)

Severity of Errors

Error category Minor Moderate Major

HWP non-intercepted errors 43 0 0CPOE non-intercepted errors 93 4 0HWP intercepted errors 7 19 0CPOE intercepted errors 2 15 3

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Rise in Mortality after CPOE

CPOE 13 months before 6 days implementation 5 months after

Subjects Children Admitted via inter-facility transport Regional, academic, tertiary-care level

children’s hospital

Han, Y. Y. et al. Paediatrics 2005;116:1506-1512

Copyright ©2005 American Academy of Pediatrics Han, Y. Y. et al. Paediatrics 2005;116:1506-1512



Inability to "preregister" patients (resolved)

Time needed to enter orders Need for a second physician Nurses away from the bedside

Changes to health care team dynamics Delays from centralization of pharmacy


EXAMPLE OF UNINTENDED CONSEQUENCES

With antibiotic administration, subsequent dosing schedules were not timed according to the time of initial dose administration but rather at predetermined default times


Same Mortality after CPOE

CPOE 13 months before 13 months after

Subjects Tertiary care PICU 20 beds 1100 annual admissions

Number of subjects 2533 children admitted 284 transported from other facilities

Campbell EM, Sittig DF, Ash JS, Guappone KP, Dykstra RH. J Am Med Inform Assoc. 2006 Sep-Oct;13(5):547-56.

Same Mortality after CPOETotal

PatientsMortality,

%Relative

Risk 95% CI P

All patients 2533 3.83 0.82 0.55–1.21 .32

Before CPOE 1232 4.22

After CPOE 1301 3.46

Transfers 284 7.75 0.66 0.29–1.47 .30

Before CPOE 125 9.60

After CPOE 159 6.29



Pre-set orders 12 infant ICU-specific

16-PICU specific Extra- corporeal life support Renal replacement therapy Complex cardiac Transplant surgery Frequent orders preset

Active involvement



Visiting prior implementation Active Involvement in design

More order sets Pre-set completed sentences Code-set filtering

Process redesign Emergency medication dispensing Pre-registering transports

Continuous improvement


EXAMPLE OF INTENDED CONSEQUENCES

The first infant transported into the ICU: the resident was able to place an entire set of orders in <5 minutes without errors in a highly stressed environment


What could go wrong?

Ash JS, Berg M, Coiera E. J Am Med Inform Assoc. 2004 Mar-Apr;11(2):104-12.

Hospital Size (beds)

CPOE System

Up Since

Percent Orders Entered

Wishard Memorial, Indianapolis, IN

340 Homegrown

1973 100%

Massachusetts General Hospital, Boston, MA

893 Homegrown

1994 100%

Faulkner Hospital, Boston, MA

150 Meditech 2003 95%

Brigham & Women's Hospital, Boston, MA

725 Homegrown

1991 90%

Alamance Regional Medical Center, Burlington, NC

238 Eclipsys 1998 95%



Unintended Consequence Frequency (%) n = 324

More/new work for clinicians 19.8Workflow issues 17.6Never ending system demands 14.8Paper persistence 10.8Changes in communication patterns and practices

10.1

Emotions 7.7New kinds of errors 7.1Changes in the power structure 6.8Overdependence on technology 5.2Total 100

What could go wrong?1. More work for clinicians

Slows speed of clinical documentation Recovers over time Learning to use CPOE takes time Excessive clinical alerts Research not related to care Poor integration of multiple systems

2. Unfavorable workflow issues3. Never ending system demands4. Problems related to paper persistence5. Untoward changes in communication patterns and

practices6. Negative emotions7. Generation of new kinds of errors8. Unexpected changes in the power structure9. Overdependence on the technology



1. More work for clinicians2. Unfavorable workflow issues

Rigid modeling of work processes Fails to support all actors Simultaneous multiple orders

3. Never ending system demands4. Problems related to paper persistence5. Untoward changes in communication patterns

and practices6. Negative emotions7. Generation of new kinds of errors8. Unexpected changes in the power structure9. Overdependence on the technology


What could go wrong?1. More work for clinicians2. Unfavorable workflow issues3. Never ending system demands

More users require more access time More software updates Overhead in maintenance Single user exceptions for order sets Changes in practice

4. Problems related to paper persistence5. Untoward changes in communication patterns and

practices6. Negative emotions7. Generation of new kinds of errors8. Unexpected changes in the power structure9. Overdependence on the technology



1. More work for clinicians2. Unfavorable workflow issues3. Never ending system demands4. Problems related to paper persistence

Integration with other clinical systems Temporary, handwritten data storage Paper reminders

5. Untoward changes in communication patterns and practices

6. Negative emotions7. Generation of new kinds of errors8. Unexpected changes in the power structure9. Overdependence on the technology



1. More work for clinicians2. Unfavorable workflow issues3. Never ending system demands4. Problems related to paper persistence5. Untoward changes in communication patterns

and practices Replaces the nexus of previously interpersonal

conversations Order entry may precede or remotely follow rounds

6. Negative emotions7. Generation of new kinds of errors8. Unexpected changes in the power structure9. Overdependence on the technology




and practices6. Negative emotions

“At first we hated every second of it.” “This is how everyone should work.”

7. Generation of new kinds of errors8. Unexpected changes in the power structure9. Overdependence on the technology


What could go wrong?1. More work for clinicians2. Unfavorable workflow issues3. Never ending system demands4. Problems related to paper persistence5. Untoward changes in communication patterns and

practices6. Negative emotions7. Generation of new kinds of errors

Problematic electronic data presentations Confusing order option presentations Inappropriate text entries Misunderstandings related to production versions Workflow process mismatches

8. Unexpected changes in the power structure9. Overdependence on the technology


What could go wrong?1. More work for clinicians2. Unfavorable workflow issues3. Never ending system demands4. Problems related to paper persistence5. Untoward changes in communication patterns and

practices6. Negative emotions7. Generation of new kinds of errors8. Unexpected changes in the power structure

Controls on who may do what and when Physicians report loss of professional autonomy Tends to encourage centralization IT department gains in power

9. Overdependence on the technologyAsh JS, Berg M, Coiera E. J Am Med Inform Assoc. 2004 Mar-Apr;11(2):104-12.



and practices6. Negative emotions7. Generation of new kinds of errors8. Unexpected changes in the power structure9. Overdependence on the technology

System failures increasingly wreak havoc May increase access to protocols & educational materials


Future

CPOE systems increase

Knowledge about use of system increases

Lessons learned are not

lost

Take Home LessonImpact on Quality is Complex

Documents

Impact of EHR on Quality of Care Farrokh Alemi, Ph.D