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IPSQWHIT: Measuring the quality improvements associated with decision support in pediatrics

IPSQWHIT: Measuring the quality improvements associated with decision support in pediatrics. AHRQ HIT Conference Timothy G. Ferris, MD, MPH Medical Director, MGPO Associate Professor of Medicine and Pediatrics Harvard Medical School. BACKGROUND.

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IPSQWHIT: Measuring the quality improvements associated with decision support in pediatrics

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  1. IPSQWHIT: Measuring the quality improvements associated with decision support in pediatrics AHRQ HIT Conference Timothy G. Ferris, MD, MPH Medical Director, MGPO Associate Professor of Medicine and Pediatrics Harvard Medical School

  2. BACKGROUND Expectations 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

  3. 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

  4. 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

  5. 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

  6. 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

  7. Weight Based Dosing Decision Support

  8. 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

  9. 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

  10. DESCRIPTION OF INTERVENTION The WBDDS included two components: • Active component: a medication menu allowing selection of a dose based on the child’s weight • 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

  11. Weight based dosing calculator Passive: Total daily dose calculated Active (part 1) Dose calculated based on patient weight Active (part 2) Select rounded dose from drop-down menu

  12. RESULTS:Dosing errors as a proportion of child office visits n=32942 (100%) • 7.7% of eligible meds had a dosing error • 1% had a dosing error >10% from recommended dose n=17526 (53%) n=3684 (11%) n=22 (.06%) n=285 (.87%) Adverse drug events All visits All visits where WBD Rx provided All visits where any Rx provided All visits with a WBD Rx error

  13. RESULTS: Rates of dosing errors for weight based dosing medications

  14. 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

  15. 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

  16. 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

  17. 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

  18. 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

  19. 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

  20. Alerts & Reminders

  21. 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

  22. 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

  23. 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)

  24. Results Management

  25. 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)

  26. Templates

  27. 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%

  28. 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 (!)

  29. 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)

  30. 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

  31. 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

  32. AHRQ Iris Mabry Jon White Co-Investigators John Co James Perrin David Bates Rainu Kaushal Eric Poon Research Assistant Sarah Johnson Acknowledgments

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