Impact Of a Clinical Decision Support Tool on Asthma Patients with Current Asthma Action Plans
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Impact Of a Clinical Decision Support Tool on Asthma Patients with Current Asthma Action Plans

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    Impact Of a Clinical Decision Support Tool on Asthma Patients with Current Asthma Action Plans Impact Of a Clinical Decision Support Tool on Asthma Patients with Current Asthma Action Plans Presentation Transcript

    • EFFECT OF COMPUTERIZED DECISION SUPPORTON PERCENT OF ASTHMA PATIENTSWITH ASTHMA ACTION PLANS Yiscah Bracha, MS, PhD Assistant Vice President Data/Analytics James M. Anderson Center for Health Systems Excellence Cincinnati Childrens Hospital Medical Center yiscah.bracha@cchmc.org Research performed at Hennepin County Medical Center, Minneapolis MN, in fulfillment of PhD requirements at the University of Minnesota, Division of Health Services Research and Policy
    • BACKGROUND & HIT ASTHMA PROJECT
    • • Generate Qs • Perform studies Researchers & Manufacturers • Generate evidence • Review evidence • Summarize results Expert Panels • Issue guidelinesKnown gap:Evidence-basedrecommendations & Informticsts • GAPphysician behavior • Hear about guidelines (maybe) Practicing • Practice medicine Physicians
    • • Generate Qs • Perform studies Researchers & Manufacturers • Generate evidence • Review evidence • Summarize results Expert Panels • Issue guidelines • Represent guidelines asHypothesis: computerized decision support (CDS)CDS can “close the Informaticists • Place CDS at point of caregap” • Invoke CDS tool while delivering care • See real-time guideline recs Practicing Physicians • Practice medicine
    • Types of CDS• Critiquing mode • Guided mode • Alerts, order sets • Decision trees at multiple • Provides: branching points • Reminders • Provides support for: • Warnings • Complex, cognitive tasks • Constrained choices (reduce • Managing chronic disease variability) • Delivery: Typical EHR • Delivery: Thru EHR system cannot deliver • Common • Uncommon
    • Approaches to CDS for chronic disease• Document-centric • User-centric • Goal: Convert guidelines • Goal: Help docs perform into code work more effectively • “Customers”: Those who • “Customers”: Physician- want docs to behave as users guidelines recommend
    • HIT Asthma ProjectA user-centric approach to asthma CDS• Initial development supported by AHRQ• Clinician requirements.  Produce a patient-specific Asthma Action Plan (AAP)  Support clinical decisions necessary to populate the AAP  Be reachable from the local EHR  Make it easy to retrieve previously created AAPs  Auto-place med order in the EHR• Implementation sites as of December 2011 • Hennepin County Medical Center. July 2009 • Fairview Health Systems. March 2010 • Altru Health System. May 2011• User stats: January – November 2011 • 6000+ AAPs; ~ 700 AAPs/month • ~ 500 active clinical users
    • HIT Asthma ProjectA user-centric approach to asthma CDS• Initial development supported by AHRQ• Clinician requirements.  Produce a patient-specific Asthma Action Plan (AAP)  Support clinical decisions necessary to populate the AAP  Be reachable from the local EHR  Make it easy to retrieve previously created AAPs Auto-place med order in the EHR• Implementation sites as of December 2011 • Hennepin County Medical Center. July 2009 • Fairview Health Systems. March 2010 • Altru Health System. May 2011• User stats: January – November 2011 • 6000+ AAPs; ~ 700 AAPs/month • ~ 500 active clinical users
    • EFFECT ONPROPORTION OF PATIENTS WITHASTHMA ACTION PLANSResults from implementation at HCMC.Weekly rates for current AAPs from March 2008 – February 2010
    • Questions and analytic strategy• Questions • What effect did CDS tool have on % of patients w/current AAPs? • If there was no effect, was this because: • Docs resisted the technology OR • Docs were not creating AAPs?• Strategy • Targeted sample for chart review (to find paper AAPs) • Calculate weekly rates for current AAPs using: • Paper template (manual) • CDS tool (automatic once decision support complete) • Either paper or CDS
    • Patients Contributing Data: Four Clinics in Two Age Groups Age on 01MAR10 Clinic School age Adult Where Patients Received CAre (5-14 years) (ge 21 years)1st Family Medicine Clinic (FM1) 93 1852nd Family Medicine Clinic (FM2) 77 1191st Pediatrics Clinic (PED1) 160 .2nd Pediatrics Clinic (PED2) 434 . Total sample is 899 patients. Sample selected for chart review (to detect presence of paper AAP) Sums across clinics exceed 899; some patients received care at multiple clinics. Responsibility for current asthma action plan is to all clinics where patients had at least one visit (any reason) from March 1, 2007 – February 9, 2010.
    • Weekly Prevalence Ratesfor Current Asthma Action PlansPediatric and Adult Asthma Patients at Four HCMC Primary Care Clinics
    • OBVIOUS EFFECTSAdults at FM1 Paper AAP forms available Clinic culture emphasized need for kids Several docs start using tool to create AAPs for adultsKids & Adults at FM2 No paper AAP forms available Docs began using CDS tool as newly available support
    • NON-OBVIOUS EFFECTSAll pediatric patients. Plans generated by paper decline as plans generated electronically increase Continued generation using CDS tool Overall effect (black line) AAPs unclear … “special cause”?
    • Specialcause?
    • Specialcause?
    • Specialcause?
    • “Special Cause” detection challenges• Data are serially correlated• Special Cause trend rules • Assume independence among successive observations • Apparent trends could be “random walks”
    • Box-Jenkins Interrupted Time Series • Auto-Regressive Integrated Moving Average (ARIMA) • Explicit model for serially correlated observations • Inclusion of “interruption” (e.g. intervention) term • Permits statistical analysis of effect of intervention compared to auto-correlated background noise Results for Children (age 5-11) With Visits at:Effect of the FM1 Peds1 Peds2intervention Coeff P-val Coeff P-val Coeff P-valInitial effect -0.601 0.49 -2.25 0.12 1.89 0.01Attenuation -0.91 <0.0001 -0.74 <0.0001 0.934 <0.0001(mult lags) 0.67 0.08 -0.64 <0.001 Initially positive,Interpretation No effect No effect oscillating thereafter
    • NON-OBVIOUS EFFECTSAll pediatric patients.AllPlans generated by paper decline as plans generated electronically increase pediatric patients. Plans generated by paper declinetoolplans generated electronically increase Continued generation using CDS as Continued generation using CDS tool Overall effect (black line) AAPs. Analysis using Interrupted TS shows: Overall effect (black line)PEDs1 unclear … “special cause”?  No effect at FM1 or AAPs  Effect in PEDs2, oscillating over time
    • Interpretation: Effect of tool on AAPs• Parsimonious theory: • Susceptible patient will receive an AAP if • Physician is “inclined” to create one AND • Support exists. Computerized tool preferred to paper template• Quality improvement implications • To increase rates of tool-generated plans, target docs who don’t create plans at all. • Provide user-centric computerized decision support
    • CO-INVESTIGATORSGail M. Brottman, MD (Hennepin County Medical Center)Angeline Carlson, PhD (Data Intelligence)Kevin Larsen, MD (Hennepin County Medical Center)
    • ACKNOWLEDGEMENTSBrendon Cullinan, MD (HealthEast)Jennifer Rodlund, RN (Hennepin Faculty Associates)Cherylee Sherry and Thouk Touch (Minneapolis Medical Research Foundation)Susan Ross, RN (Minnesota Department of Health)The IT and EHR team at Hennepin County Medical CenterDonald Uden, PharmD (University of Minnesota)Robert Grundmeier, MD (The Children’s Hospital of Philadelphia)Tim Michalski (Point of Care Decision Support)Robert Mayes, RN (AHRQ and American Medical Informatics Association)