Slide Presentation from the AHRQ 2008 Annual Conference
On September 8, 2008, Timothy G. Ferris, M.D., M.P.H., made this presentation at the 2008 Annual Conference. Select to access the PowerPoint® presentation (280 KB; Plugin Software Help).
IPSQWHIT: Measuring the Quality Improvements Associated with Decision Support in Pediatrics
Timothy G. Ferris, MD, MPH
Medical Director, MGPO
Associate Professor of Medicine and Pediatrics
Harvard Medical School
Expectations for the ability of electronic health records (EHRs) to improve quality are based on potential of decision support.
- Slow adoption is a barrier:
Paper → HER → DS
- Evidence of improved quality summarized in Shekelle/AHRQ Evidence Report:
- Significant improvements in quality.
- Difficult to aggregate and/or generalize.
- Office of the
National Coordinator for Health Information Technology (ONC) Report on measuring quality benefits of Health Information Technology (Health IT):
Improving Patient Safety and Quality With Health Information Technology
- Funded by the Agency for Healthcare Research and Quality (AHRQ) Health IT Value Request for Applications (RFA):
- Focused on improvements in safety, quality, and efficiency through Health IT.
- Several pediatric grants funded.
- 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 Web site.
Prioritizing Decision Support
- MD use of DS has limits:
- Need to prioritize what is asked of them.
- Grounds for prioritization:
- 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 (WBDDS)
Medication Errors in Pediatrics
- Medication errors among the most common and most injurious of all errors in health care.1
- 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.
1 Bates 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
- 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:
- 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.
Weight based dosing (WBD) calculator
Two screen shots showing the dosing calculator for Amoxicillin.
- 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
Screen shot of a diagram showing nested rectangles and squares.
- All visits: n=32942 (100%).
- All visits where any Rx provided: n=17526 (53%).
- All visits where WBD Rx provided: n=3684 (11%).
- All visits with a WBD Rx error: n=285 (.87%).
- Adverse drug events: n=22 (.06%).
- 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
|Dosing Error Type
Change in frequency
per 100 prescriptions
Change in frequency
per 100 prescriptions
|All Dosing Errors
- 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.
- 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.
- 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 adverse
drug events (ADEs).
- 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.
- 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.
- 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
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.
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 primary care physician (PCP).
- Lipid profile every 2 years for patients with Body Mass Index (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).
Table 1. Goals for quality improvement with an electronic results management system
|Institute of Medicine (IOM) Quality Aims
||Problems associated with results management
||Goals for electronic results management
||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)
||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)
||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)
||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)
||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)
||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
- 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)
- 32% of attention deficit hyperactivity disorder (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
- 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 Smart Form (continued)
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).
- 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: (continued)
QI/Health IT 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.
- John Co
- James Perrin
- David Bates
- Rainu Kaushal
- Eric Poon
Current as of January 2009
IPSQWHIT: Measuring the Quality Improvements Associated with Decision Support in Pediatrics. Slide Presentation from the AHRQ 2008 Annual Conference (Text Version). January 2009.
Agency for Healthcare Research and Quality, Rockville, MD. http://www.ahrq.gov/about/annualmtg08/090808slides/Ferris.htm