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Supplements Special Issue: Health Information Technology — Guest Editors: Sachin H. Jain, MD, MBA; and David B
The Road to Electronic Health Records Is Paved With Operations
Amir Dan Rubin, MBA, MHSA; and Virginia A. McFerran, MA
Finding Cancer at Home
Katlyn L. Nemani, BA
Alternative Measures of Electronic Health Record Adoption Among Hospitals
Fredric E. Blavin, MS; Melinda J. Beeuwkes Buntin, PhD; and Charles P. Friedman, PhD
Physician ePortfolio: The Missing Piece for Linking Performance With Improvement
Nancy L. Davis, PhD; Lloyd Myers, RPh; and Zachary E. Myers
Using Electronic Prescribing Transaction Data to Estimate Electronic Health Record Adoption
Emily Ruth Maxson, BS; Melinda J. Beeuwkes Buntin, PhD; and Farzad Mostashari, MD, ScM
Understanding Meaningful Outcomes
Daniel C. Armijo, MHSA; Eric J. Lammers, MPP; and Dean G. Smith, PhD
Electronic Health Record Feedback to Improve Antibiotic Prescribing for Acute Respiratory Infections
Jeffrey A. Linder, MD, MPH; Jeffrey L. Schnipper, MD, MPH; Ruslana Tsurikova, Msc, MA; D. Tony Yu, MD, MPH; Lynn A. Volk, MHS; Andrea J. Melnikas, MPH; Matvey B. Palchuk, MD, MS; Maya Olsha-Yehiav, MS
Review of Veterans Health Administration Telemedicine Interventions
Robert D. Hill, PhD; Marilyn K. Luptak, PhD, MSW; Randall W. Rupper, MD, MPH; Byron Bair, MD; Cherie Peterson, RN, MS; Nancy Dailey, MSN, RN-BC; and Bret L. Hicken, PhD, MSPH
Achieving Meaningful Use: A Health System Perspective
Cynthia L. Bero, MPH; and Thomas H. Lee, MD
Health Information Technology Is Leading Multisector Health System Transformation
Sachin H. Jain, MD, MBA; and David Blumenthal, MD, MPP
Health Information Technology and Health System Redesign-The Quality Chasm Revisited
Reed V. Tuckson, MD; Deneen Vojta, MD; and Andrew M. Slavitt, MBA
Health Information Technology and the Medical School Curriculum
Marc M. Triola, MD; Erica Friedman, MD; Christopher Cimino, MD; Enid M. Geyer, MLS, MBA; Jo Wiederhorn, MSW; and Crystal Mainiero
Congressional Intent for the HITECH Act
Pete Stark
Optimizing Health Information Technology's Role in Enabling Comparative Effectiveness Research
Amol S. Navathe, MD, PhD; and Patrick H. Conway, MD, MSc
Healthcare Information Technology Interventions to Improve Cardiovascular and Diabetes Medication Adherence
Alexander S. Misono, BA; Sarah L. Cutrona, MD, MPH; Niteesh K. Choudhry, MD, PhD; Michael A. Fischer, MD, MS; Margaret R. Stedman, PhD; Joshua N. Liberman, PhD; Troyen A. Brennan, MD, JD; Sachin H. Ja
Uniting the Tribes of Health System Improvement
Aaron McKethan, PhD; and Craig Brammer
Electronic Health Record Adoption and Quality Improvement in US Hospitals
Spencer S. Jones, PhD; John L. Adams, PhD; Eric C. Schneider, MD; Jeanne S. Ringel, PhD; and Elizabeth A. McGlynn, PhD
A Health Plan Prescription for Health Information Technology
Newt Gingrich, PhD, MA; and Malik Hasan, MD
HITECH Lays the Foundation for More Ambitious Outcomes-Based Reimbursement
John Glaser, PhD
Increasing Consumerism in Healthcare Through Intelligent Information Technology
Seth B. Cohen, MBA, MPA; Kurt D. Grote, MD; Wayne E. Pietraszek, MBA; and Francois Laflamme, MBA
Electronic Health Records: Potential to Transform Medical Education
Bryant A. Adibe, BS; and Sachin H. Jain, MD, MBA
Smart Health Community: The Hidden Value of Health Information Exchange
James N. Ciriello, MS; and Nalin Kulatilaka, PhD, MS
Currently Reading
Effects of Documentation-Based Decision Support on Chronic Disease Management
Jeffrey L. Schnipper, MD, MPH; Jeffrey A. Linder, MD, MPH; Matvey B. Palchuk, MD, MS; D. Tony Yu, MD; Kerry E. McColgan, BA; Lynn A. Volk, MHS; Ruslana Tsurikova, MA; Andrea J. Melnikas, BA; Jonathan

Effects of Documentation-Based Decision Support on Chronic Disease Management

Jeffrey L. Schnipper, MD, MPH; Jeffrey A. Linder, MD, MPH; Matvey B. Palchuk, MD, MS; D. Tony Yu, MD; Kerry E. McColgan, BA; Lynn A. Volk, MHS; Ruslana Tsurikova, MA; Andrea J. Melnikas, BA; Jonathan

A trial of electronic note–based decision support showed small effects on management of patients with heart disease and diabetes, mostly because it was infrequently used.

Objective: To evaluate whether a new documentation-based clinical decision support system (CDSS) is effective in addressing deficiencies in the care of patients with coronary artery disease (CAD) and diabetes mellitus (DM).


Study Design: Controlled trial randomized by physician.


Methods: We assigned primary care physicians (PCPs) in 10 ambulatory practices to usual care or the CAD/DM Smart Form for 9 months. The primary outcome was the proportion of deficiencies in care that were addressed within 30 days after a patient visit.


Results: The Smart Form was used for 5.6% of eligible patients. In the intention-to-treat analysis, patients of intervention PCPs had a greater proportion of deficiencies addressed within 30 days of a visit compared with controls (11.4% vs 10.1%, adjusted and clustered odds ratio =1.14; 95% confidence interval, 1.02-1.28; P = .02). Differences were more pronounced in the “on-treatment” analysis: 17.0% of deficiencies were addressed after visits in which the Smart Form was used compared with 10.6% of deficiencies after visits in which it was not used (P <.001). Measures that improved included documentation of smoking status and prescription of antiplatelet agents when appropriate.


Conclusions: Overall use of the CAD/DM Smart Form was low, and improvements in management were modest. When used, documentation-based decision support shows promise, and future studies should focus on refining such tools, integrating them into current electronic health record platforms, and promoting their use, perhaps through organizational changes to primary care practices.


(Am J Manag Care. 2010;16(12 Spec No.):SP72-SP81)

  • A novel note-based decision support system built into an electronic health record was associated with an increase in the proportion of deficiencies in the management of heart disease and diabetes addressed within 30 days of a visit in which it was used.


  • However, because it was used in only 5.6% of eligible patients, its overall effect on care was small (absolute increase of 1.3% of deficiencies addressed).


  • Improvements in chronic disease management likely require financial incentives to improve care, multifaceted quality improvement efforts, distribution of work to a patient care team, and health information tools that support these activities.
Clinical decision support systems (CDSSs) integrated within electronic health records (EHRs) hold the promise of improving healthcare quality, but to date the effectiveness of CDSSs and EHRs has been less than expected, especially with respect to the ambulatory management of chronic diseases.1,2 In part this is because clinicians do not use CDSSs fully, if at all. In fact, several studies of CDSSs show low rates of use among clinicians.3-7 Barriers to clinicians’ use of CDSSs have included lack of integration into work flow, software usability issues, and relevance of the content to the patient at hand.8 At Partners HealthCare, we developed Smart Forms to facilitate documentation-based clinical    decision support. Rather than being interruptive in nature, the Smart Form enables writing a multiproblem visit note while capturing coded information and providing sophisticated decision support in the form of tailored recommendations for care. The most recent version of the Smart Form was designed around 2 chronic diseases: coronary artery disease (CAD) and diabetes mellitus (DM).9

In a previous study, we conducted pilot testing with 30 clinicians during a 6- to 8-week period.10 When deficiencies in CAD/DM management were present, they were more often addressed in the month following visits in which the Smart Form was used compared with preintervention visits. Specific deficiencies that were more often addressed included  documentation of blood pressure, smoking status, height and weight, and prescription of beta-blockers.

To more rigorously assess the effect of the intervention in a broader patient population, we conducted a randomized controlled trial (RCT) of the CAD/DM Smart Form and evaluated its effects on chronic disease management.



We conducted a controlled trial, randomized by provider, in primary care clinics associated with Partners HealthCare System. The Partners Human Research Committee approved

the study.

Setting and EHR

Partners HealthCare System is an integrated regional healthcare delivery network in eastern Massachusetts. Partners includes more than 20 primary care clinics affiliated with Brigham and Women’s Hospital or Massachusetts General Hospital, 5 acute care hospitals, several specialty and rehabilitation hospitals, and other affiliated ambulatory practices. The main EHR used in Partners ambulatory clinics is the Longitudinal Medical Record (LMR), a proprietary, Certification Commission for Healthcare Information Technology–certified EHR.11

CAD/DM Smart Form

The CAD/DM Smart Form has been described previously9 (see Figure). The goal of Smart Forms was to integrate clinical data display, clinical decision support, ordering, and  documentation. The Smart Form is a documentation tool, and as such, has many features in common with other latest- generation EHRs, including the ability to add, edit, and delete coded and/or structured clinical information such as medical problems, medications, and vital signs, and to easily  import that information into a visit note. Like some systems that use disease-specific templates,12 the Smart Form organizes clinical data around certain diseases to facilitate decision making and also highlights and “requests” missing coded information such as blood pressure, height, weight, and smoking status.

The Smart Form also is a CDSS and as such generates output that integrates patient demographic and clinical data with rule-based logic derived from guidelines for the management of CAD and DM.9 The output includes assessments  of the current state of clinical care (eg, low-density lipoprotein cholesterol above the goal of 100 mg/dL) and suggested orders for medication additions or changes, laboratory studies, appointments and referrals, and printing of patient educational materials. If a suggested order is selected by the user, the action is carried out (ie, it is linked to provider order entry, such as prescription writing), and the EHR is automatically updated (ie, the medication list reflects the change). In addition, the selected action can be easily added to the note with a few keystrokes or mouse clicks (see Figure).

Ideally, the Smart Form would replace the users’ usual note-writing tools, including the standard free text or template- based note-writing function within the LMR for all patients with the conditions supported by it. In its current form, the Smart Form has to be actively chosen by the user when beginning a note-writing session.

Clinicians and Patients

We recruited 10 adult primary care practices at Brigham and Women’s Hospital and Massachusetts General Hospital that use the LMR out of a total of 15 practices that were invited. Practices that agreed to participate were informed about the Smart Form and told that primary care physicians (PCPs) would be randomized to receive it or usual care. Eligible patients were defined as those with CAD or DM who had a visit with a PCP who belonged to 1 of the study practices from the date the practice was given the Smart Form until the end of the study period 9 months later. Practices received the Smart Form on a rolling basis from March 3, 2007, through August 10, 2007. To qualify, patients had to have CAD or DM on their EHR problem list as of the day prior to the start date of the RCT for that practice (see Table 1 for a list of qualifying conditions). We previously found these definitions of CAD and DM to have a positive predictive value of 94% and 96%, respectively.13

Primary care physicians were assigned to receive the Smart Form or usual care on the basis of random number generation in Microsoft Excel (Redmond, WA). Those PCPs assigned to the intervention arm were notified by e-mail and received brief instruction on the use of the Smart Form at an on-site practice meeting. A computerized video tutorial about the Smart Form could be accessed at any time from within the application’s help menu. In addition, we took several steps to better engage clinicians in Smart Form use:

• We returned to each clinic to meet again with clinicians, encouraged use of the Smart Form, and performed on-site training, emphasizing integration into clinicians’ existing work flow.

• We tracked use by clinician and sent customized emails every 1-2 months to PCPs depending on whether they used the Smart Form frequently, infrequently, or never, reminding them to use it, encouraging use, and soliciting feedback on usability, as appropriate.

• We identified and contacted frequent users to find out why they liked the Smart Form to discover ways those lessons could be communicated to other intervention PCPs.

• Halfway through the study, we began sending monthly Tips for Users by e-mail, highlighting appealing but less obvious features of the Smart Form or ways to address potential usability issues mentioned by other users.

Outcomes and Data Sources

The primary outcome was the mean percentage of deficiencies in CAD/DM management addressed within 1 month of an index visit (ie, any visit by an eligible patient to their PCP during the study period). There were 9 possible performance measures for which patients with CAD could have a deficiency, including documentation measures such as smoking status, medication measures such as antiplatelet use, and management measures such as blood pressure control to recommended goals. Measures for patients with DM included the same measures for CAD except for antiplatelet and betablocker use, plus 6 others (see Table 1 for definitions).

Care management deficiencies were identified by querying the EHR and Partners Clinical Data Repository as of the day prior to the index visit. If the deficiency was present, we then queried the same data sources 30 days after the index visit to evaluate whether the deficiency had been addressed. Deficiencies were considered addressed if missing or out-of-date documentation was subsequently supplied, an indicated medication was prescribed or a contraindication documented, or action was taken in response to suboptimal management (see Table 1).

For each index visit, we analyzed whether each applicable deficiency was addressed and the percentage of applicable deficiencies that were addressed. As a secondary analysis, we evaluated the proportion of management goals met as of the day prior to the RCT study period and the last day of the study period for each practice (ie, patient-level as opposed to visit-level analysis).

Data Analysis

Baseline characteristics of clinicians and patients were analyzed using standard descriptive statistics. The primary outcome was the mean percentage of deficiencies in care management addressed per visit in an intention-to-treat analysis. In other words, outcomes of all patients assigned to intervention PCPs were compared with outcomes of all patients assigned to usual care PCPs, regardless of whether the PCP used the Smart Form at a given visit or with a given patient. The primary outcome was analyzed using binomial logistic regression (ie, with the dependent variable in the form X/N, where N equals the number of deficiencies and X equals the number of deficiencies addressed). To adjust for potential confounders, we created multivariable models that included all patient- and provider-level covariates that were significant predictors of the outcome from bivariable testing at a P <.10 level. Nonsignificant collinear terms then were removed from the final model for parsimony. We used generalized estimating equations (with PROC GENMOD in SAS 9.1 [SAS Institute, Inc, Cary, NC]) to adjust for clustering of patients by provider (ie, taking into account the fact that outcomes were analyzed at the visit or patient level while randomization occurred at the provider level and that patients of one provider might be more similar to one another than patients of other providers). These analyses were repeated for each individual quality measure using logistic regression models.

For patient-level analyses, binomial logistic regression was used in which X/N represented the total number of goals met over the number of applicable goals for that patient (ie, depending on the patient’s conditions). Repeated-measures analysis was conducted to evaluate whether the intervention was associated with greater improvement in the proportion of goals met at the end of the study compared with the beginning (ie, reported P values are for the interaction term of [intervention arm]*[time]).

Last, we repeated the above using “on-treatment” analyses, comparing outcomes for patients of PCPs who used the Smart Form during a given visit (in visit-level analyses) or with a given patient at all (for patient-level analyses) with outcomes for both control patients and patients of intervention PCPs for whom the Smart Form was not used. These 2 “nonuse” groups were combined in some analyses to evaluate the overall association between Smart Form use and outcomes. Unless otherwise stated, 2-sided P values less than .05 were considered significant. All analyses were conducted using SAS version 9.1.


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