IPSQWHIT: Measuring the quality

improvements associated with decision support in pediatrics

AHRQ HIT ConferenceTimothy G. Ferris, MD, MPH

Medical Director, MGPOAssociate Professor of Medicine and Pediatrics

Harvard Medical School

BACKGROUNDExpectations for the ability of EHRs to improve quality

are based on potential of decision support

• Slow adoption is a barrier Paper EHR DS

• Evidence of improved quality summarized in Shekelle/AHRQ Evidence Report

• Significant improvements in quality• Difficult to aggregate and/or generalize

• ONC Report on measuring quality benefits of HIT– http://www.rwjf.org/files/research/3297.31831.hitreport.pdf

IPSQWHIT

• Improving Patient Safety and Quality With Health Information Technology– Funded by the Agency for Healthcare Research

and Quality (AHRQ) HIT Value RFA

• Focused on improvements in safety, quality, and efficiency through HIT

• Several pediatric grants funded

IPSQWHIT

• e-Rx decision support: Weight based dosing• Reminders (synchronous and asynchronous)

• Results management

• Templates (acute and chronic conditions)

All specifications and templates available on AHRQ website

Prioritizing Decision Support

• MD use of DS has limits– Need to prioritize what is asked of them

• Grounds for prioritization:– Safety– Clinical impact

• Evidence for improvement– Common problem/small impact– Rare problem/large impact

Design and Setting

• Group randomized trials conducted within Partners Healthcare in Eastern MA.

• 26 pediatric practices– All participants had already adopted the same EHR – Hospital based (1), health center (6), private (19)– Participation depended on multiple factors– Once selected, sites were paired by type and randomized

to intervention or control.– Analyses adjusted for clustering by MD and practice

Weight Based Dosing Decision

Support

Medication Errors in Pediatrics

• Medication errors among the most common and most injurious of all errors in health care1

• Pediatric prescriptions may be more prone to error

• Limited data on rates of pediatric dosing errors

• Unclear if computerized decision support in the context of electronic prescribing reduces weight related dosing errors

1Bates DW, et al, JAMA. 1998; Dean B, et al. Qual Saf Health Care. 2002; Kaushal R, et al. JAMA. 2001 2 Sullivan JE, et al. J Surg Oncol. 2004

OBJECTIVES

• To examine the prevalence of dosing errors in ambulatory pediatrics

• To examine the effectiveness of weight based dosing decision support in reducing the frequency and severity of dosing errors

DESCRIPTION OF INTERVENTION

The WBDDS included two components:1. Active component: a medication menu allowing

selection of a dose based on the child’s weight 2. Passive component: display of a computer generated

total daily dose in mg/kg based on the child’s weight

Child’s most recent weight imported from the EHR

Weight based dosing calculator

Active (part 1) Dose calculated based on patient weight

Passive: Total daily dose calculated

Active (part 2) Select rounded dose from drop-down menu

RESULTS:Dosing errors as a proportion of child office visits

n=32942 (100%)

n=17526 (53%)

n=3684 (11%)

n=285 (.87%)

n=22 (.06%)

All visitsAll visits where any Rx provided

All visits where WBD Rx provided

Adverse drug events

All visits with a WBD Rx error

•7.7% of eligible meds had a dosing error•1% had a dosing error >10% from recommended dose

RESULTS: Rates of dosing errors for weight based dosing medications

ControlChange in frequency of

errorsper 100 prescriptions

InterventionChange in frequency of

errorsper 100 prescriptions

p

All Dosing Errors +0.6 -3.8 .003Overdoses (all) -1.05 -2.96 .02Overdoses (antibiotics)

+0.99 -2.65 <.0001

Overdoses (other) -2.13 -0.31 0.1Under doses (all) +.17 -0.8 0.006

RESULTS: • Physicians prescribed antibiotics more than any

other type of medication and antibiotics were the most likely medication to include a dosing error

• The active decision support (WBDDS) was used for approximately 10% of Rx in intervention group

– No dosing errors when active decision support was used

RESULTS:• Majority of dosing errors (58%) judged to be

correctable with use of decision support

• 22% of dosing errors considered directly attributable to incorrect use of the electronic prescribing software

• Interviews revealed a number of barriers: technical difficulties, user interface challenges, and negative physician perceptions

LIMITATIONS• Prescribing software did not accommodate

medications requiring variable dosing or combination medications– Significant source of dosing errors

• Unable to fully assess physician use

• No systematic assessment for ADE’s

CONCLUSIONS

• Dosing errors represent a substantial fraction of medication errors in pediatrics– 10% of eligible Rx– National extrapolation: approximately 4,000,000 dosing errors in

weight based dosing eligible pediatric prescriptions every year

• WBDDS reduced dosing errors from 10.0 per 100 scripts to 6.3 per 100 scripts – National extrapolation: reduction of over 150,000 dosing errors per

year

• Real world effectiveness vs. ideal world efficacy

CONCLUSIONS• Very few adverse drug events associated with

these dosing errors

• No incorrectly dosed prescriptions when the active form of WBDDS was used

• Difficulties using software were a major barrier to regular use of the active DS

IMPLICATIONS• Weight based dosing decision support led to

reductions in the overall dosing error rate and for overdoses in particular

• New errors caused by electronic prescribing software

• Full benefit of e-prescribing will require WBDDS designed to accommodate physician workflow

Alerts & Reminders

Reminders• ADHD: % of patients receiving follow-up care

every 6 months

– Rates: 53.9% (Cont) vs. 70.1% (Int) (p=.04)• 33.5% vs. 43.7% at ADHD visit (p=.27)• 22.3% vs. 28.2% at Well child check (p=.33)

– Intervention patients were 2.1 times as likely to have had appropriate follow-up

Reminders• Chlamydia: annual screening test for patients

who are sexually active

– Rates: 24% (Control) vs. 48% (Intervention)

– 61% of screening tests were ordered by the patient’s PCP

Reminders• Obesity:

– Lipid profile every 2 years for patients with BMI >99th percentile

• 23 of 200 patients (11.5%) received a lipid profile

• No significant difference between control and intervention (13 intervention vs.10 control)

– Follow-up visit every six months patients with BMI >95th percentile

• 75% of intervention group patients had visit where nutritional habits were reviewed vs. 71% in the control (p=.5)

Results Management

Table 1. Goals for quality improvement with an electronic results management system

IOM Quality

Aims

Problems associated with results

management

Goals for electronic results management

Efficiency Significant provider/administrative time

spent tracking lab results and fielding

calls from parents looking for results;

significant costs associated with

performance of redundant tests

Reduced costs spent on paper (PRA); reduced

trash (PRA);

re-allocation of staff time (PRA, DS); savings due

to reductions in ordering of duplicate tests (PRA)

Timeliness Delays in receipt and/or review of

critical lab results; delays in patient

notification regarding lab results

More timely access to lab results (PRO, DS);

reduced delays in patient notification and/or

intervention (PA, DS)

Safety Delays in receipt and/or review of

critical lab results; poor communication

between providers regarding appropriate

follow-up

Increased timeliness and access to lab results

leads to improvements in patient safety (PA);

improved documentation in EHR (PA, PRA,

PRO, DS)

Effectiveness Incomplete records/inability to access or

locate previous lab results; poor

communication between providers

regarding appropriate follow up

Improved access to previous lab results promotes

guideline adherence (PA, PRO, DS); enabling

providers to document detailed instructions for

corollary/follow-up care in record (to a nurse)

(PA, PRO, PRA, DS)

Patient-

centeredness

Incomplete records/inability to access or

locate previous lab results; delays in

patient notification regarding lab results

Improved provider/patient communication

regarding lab results (PA, PRA, PRO); decreased

delays in patient notification (PA, PRO, PRA,

DS)

Equity Lack of standardized systems for results

management; reliance on individual

provider to manage tracking and follow-

up of lab results; language barriers may

hinder communication of results

Standardized notifications promote more

equitable treatment (PA, DS); Allows providers

to better communicate results to patients/parents

whose primary language is not English

Results Management• Main findings:

– Full adoption practices reported gains in efficiency, reliability, timeliness, and provider satisfaction

– Some partial adopters reported decreased efficiency and increased risk of lost test results

– Barriers to ERM adoption included lack of inclusion of all ordered tests in the ERM system, user-interface design issues, and lack of sufficient pediatric customization

Ferris et al, Pediatrics (in press)

Templates

Templates• ADHD

– Usage: • 32% of ADHD specific visits at intervention clinics

– Documentation quality: • Documentation of symptoms: 96.6% (T) vs. 29%

• Treatment effectiveness: 100% (T) vs. 61.3%

• Treatment side effects: 96.6% (T) vs. 54.8%

ARI Smart Form• Usage:

– Successfully used at 561 ARI visits to treat 522 individual patients with 680 primary and secondary diagnoses

– The Smart form was employed by 39 providers with a median number of uses/user of 18 (range 1-109)

– Used for only 8% of all eligible visits (!)

ARI Smartform• Changes in prescribing:

– In the intervention group, fewer antimicrobial prescriptions were written when the SF was used:

• 31.7% (SF) vs. 39.9% (p<.0007)

– Providers using the SF were less likely to recommend a macrolide antibiotic

• 6.2% of ARI visits vs. 9.5%, p=.022

– Providers also prescribed fewer antibiotics for viral ARI illnesses when utilizing the SF

• 12.3% of viral ARI visits versus 18.1% of viral ARI illnesses; p=.0125)

Lessons learned:• Clinical perspective

– Outpatient pediatric workflow necessitates tools designed specifically for that population and setting

– Clinicians respond with variable frequency to prompts to perform preventive care measures

– Reminders promote effective management of chronic conditions at well child visits (well child templates might inhibit documentation)

– Smartform lead to increased guideline adherence for acute illness care

Lessons learned:• QI/ HIT perspective

– Administrative/organizational barriers are substantial

– Effective design requires cooperation from practice administrators, IT personnel, network leadership, and clinicians—also iterative modification as guidelines change

– Variation in clinical workflow across ambulatory settings necessitates the tools that can be easily modified

– Given the impact of perceived value on use, provider training and education appear an integral component of implementation

Acknowledgments

• AHRQ– Iris Mabry– Jon White

• Co-Investigators– John Co– James Perrin– David Bates– Rainu Kaushal– Eric Poon

• Research Assistant– Sarah Johnson