EFFECT OF COMPUTERIZED DECISION SUPPORT ON PERCENT OF ASTHMA PATIENTS WITH 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

Known gap:Evidence-based recommendations & physician behavior

Researcher

s &

Manufacture

rs

• Generate Qs• Perform studies• Generate evidence

Expert

Panels

• Review evidence• Summarize results• Issue guidelines

Informticsts

•GAP

Practicing

Physicians

• Hear about guidelines (maybe)• Practice medicine

Hypothesis: CDS can “close the gap”

Researcher

s &

Manufacture

rs

• Generate Qs• Perform studies• Generate evidence

Expert

Panels

• Review evidence• Summarize results• Issue guidelines

Informaticist

s

• Represent guidelines as computerized decision support (CDS)

• Place CDS at point of care

Practicing

Physicians

• Invoke CDS tool while delivering care• See real-time guideline recs• Practice medicine

Types of CDS

• Critiquing mode• Alerts, order sets• Provides:

• Reminders• Warnings• Constrained choices (reduce

variability)

• Delivery: Thru EHR• Common

• Guided mode • Decision trees at multiple

branching points• Provides support for:

• Complex, cognitive tasks• Managing chronic disease

• Delivery: Typical EHR system cannot deliver

• Uncommon

Approaches to CDS for chronic disease

• Document-centric• Goal: Convert guidelines

into code• “Customers”: Those who

want docs to behave as guidelines recommend

• User-centric• Goal: Help docs perform

work more effectively• “Customers”: Physician-

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

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 AAPBe reachable from the local EHRMake it easy to retrieve previously created AAPsAuto-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 ON PROPORTION OF PATIENTS WITH ASTHMA ACTION PLANS

Results 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

Clinic Where Patients Received CAre

Age on 01MAR10

School age (5-14 years)

Adult (ge 21 years)

1st Family Medicine Clinic (FM1) 93 185

2nd Family Medicine Clinic (FM2) 77 119

1st Pediatrics Clinic (PED1) 160 .

2nd Pediatrics Clinic (PED2) 434 .

Patients Contributing Data:Four Clinics in Two Age Groups

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 Rates for Current Asthma Action PlansPediatric and Adult Asthma Patients at Four HCMC Primary Care Clinics

Tool introduced

OBVIOUS EFFECTS

Adults at FM1 Paper AAP forms available Clinic culture emphasized need for kids Several docs start using tool to create AAPs for adults

Kids & Adults at FM2 No paper AAP forms available Docs began using CDS tool as newly available support

NON-OBVIOUS EFFECTS

All 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”?

Special cause?

Special cause?

Special cause?

“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

Effect of the intervention

Results for Children (age 5-11) With Visits at:

FM1 Peds1 Peds2

Coeff P-val Coeff P-val Coeff P-val

Initial effect -0.601 0.49 -2.25 0.12 1.89 0.01

Attenuation (mult lags)

0.934 <0.0001-0.91 <0.0001 -0.74 <0.0001

0.67 0.08 -0.64 <0.001

Interpretation No effect No effect Initially positive, oscillating thereafter

NON-OBVIOUS EFFECTS

All 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”?

All pediatric patients. Plans generated by paper decline as plans generated electronically increase Continued generation using CDS tool Overall effect (black line) AAPs. Analysis using Interrupted TS shows:

No effect at FM1 or PEDs1 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)

ACKNOWLEDGEMENTS

Brendon 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 Center

Donald 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)