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Real-Time Decision and Documentation Support Increases Adherence to Recommended Care for Respiratory Infections, Diabetes, and Heart Disease


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Summary

Partners HealthCare System seeks to ensure appropriate care for patients with acute respiratory infections, coronary artery disease, and diabetes by providing real-time clinical decision and documentation support through the system’s electronic medical record. A "Smart Form" provides real-time reminders to physicians about guideline-based care recommendations for these patients, and automatically documents care provided within the clinical notes section of the medical record. The system improved the appropriateness of antibiotic prescribing for acute respiratory infections and increased use of recommended therapies and improved documentation for patients with coronary artery disease and diabetes.

Evidence Rating (What is this?)

Strong: The evidence consists of RCTs and pre- and post-implementation comparisons of key outcomes measures, including the proportion of care deficiencies addressed, rates of appropriate antibiotic prescribing, adjustments in diabetes therapy, prescribing of antiplatelet therapy and beta blocker medications, and documentation of key metrics in the EHR.
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Developing Organizations

Partners Healthcare System
Boston, MAend do

Date First Implemented

2004

Problem Addressed

Physicians frequently fail to adhere to recommended clinical care for patients with coronary artery disease (CAD), diabetes, and acute respiratory infections. Electronic health records (EHRs) with decision support functionality can help physicians follow guidelines and hence avoid these "care deficiencies," but this type of support is seldom easy to use or readily available at the point of care.
  • Failure to follow recommended care: Physicians frequently fail to provide recommended care for CAD, diabetes, and acute respiratory infections, resulting in "care deficiencies." For example, acute respiratory infections account for approximately half of all antibiotic prescriptions written for adults, yet studies have found many of these prescriptions to be inappropriate, either because the infection is viral (rendering antibiotics ineffective) or the antibiotic is too strong for the bacteria being treated.1 Another study found that only a third of primary care visits involving patients with diabetes resulted in the provision of five recommended services (foot examination, referral for an eye examination, blood glucose measurement, a lipid panel, and a urine microalbumin test).2
  • Unrealized potential of decision support: While clinical decision support systems can help clinicians identify opportunities to improve disease management, real-world use of these systems has resulted in minimal improvements in patient outcomes. Key factors in this underperformance include poor timing, little or no integration of decision support into existing workflow, lack of relevance to the patient’s condition or care needs, the failure to link decision support and clinical action (e.g., medication prescribing or test ordering), and lack of time savings or other efficiencies for physicians using such systems.3 Correcting these shortcomings—for example, by providing real-time decision support at the point of care and automatically documenting the provision of care in the patient’s record—could increase the use and effectiveness of these systems.

What They Did

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Description of the Innovative Activity

Partners HealthCare System uses a "Smart Form" within the EHR to provide real-time reminders to physicians about guideline-based care recommendations for patients with acute respiratory infections, CAD, and diabetes. For CAD and diabetes, the system identifies appropriate tests and medical treatment, while for acute respiratory infection the system helps physicians prescribe antibiotics appropriately. In all cases, the system automatically documents care provided within the clinical notes section of the patient’s medical record. Key elements of the system are described below:
  • Point-of-care access: The physician accesses the Smart Form from the EHR’s “notes” section while conducting the patient visit, a time when the physician will likely be receptive to clinical suggestions.
  • Easily reviewable background information: Physicians review the left side of the form, which contains critical background information about the patient’s health, including allergies, medications, vital signs, and laboratory test results. Having all relevant information in one place facilitates the physician’s understanding of the patient’s health status.
  • Easy-to-complete documentation: The physician interviews the patient about his or her symptoms and medical history and reviews the patient’s medication list, confirming its accuracy and deleting or adding medications as specified by the patient. During the physical examination, the physician documents all relevant observations and information by clicking preexisting boxes, choosing statements from drop-down menus, and/or entering free text. All information becomes part of the patient’s record and is available to aid in current and future decisionmaking.
  • Automatic recommendations on care needs: The system automatically generates recommended tests and treatments for the physician's consideration based on the available information and established guidelines, as outlined below:
    • For CAD and diabetes: The system presents a list of care options for the physician’s consideration. For example, if a patient with CAD has low-density lipoprotein levels that are above goal despite being on a statin, the system offers various options, including changing therapy (e.g., increasing dosage of the current drug, initiating fibrate therapy, and/or changing to a different statin), ordering laboratory tests (e.g., lipid panel, liver function), and/or referring the patient to another clinician (e.g., nutritionist, lipid specialist). The physician checks desired actions on the list. Suggested actions cover glycemia therapy, cholesterol management, antiplatelet therapy, blood pressure management, diabetic foot examination, weight management, and cigarette smoking.
    • Acute respiratory infections: After physicians document patient symptoms using a checklist, the acute respiratory infection form presents possible diagnoses and treatment options appropriate for each. The physician selects the suspected diagnosis and is presented with testing options (e.g., a throat culture or a rapid strep test) and potential treatments, including prescription and/or over-the-counter medications appropriate for that diagnosis. For example, if the physician enters a diagnosis for which antibiotic therapy is appropriate (e.g., streptococcal pharyngitis), the physician sees a list of antibiotic options for that diagnosis. However, if the physician selects a diagnosis for which antibiotics are not indicated, such as the common cold or acute bronchitis, the physician sees a statement confirming the inappropriateness of antibiotic therapy along with a list of potential therapies to control symptoms.
    • Easy documentation of actions in visit note: Information provided in June 2012 indicates that any recommended actions selected by the clinician can be added to the visit note with one click, thus saving the clinician time and providing documentation that appropriate actions were taken during the visit.
  • Identifying and addressing care deficiencies: The form identifies any cases in which recommended care has not been provided (e.g., a comprehensive foot examination for a diabetes patient in the past 12 months) and prompts the physician to address the deficiencies, either by providing the recommended care, prescribing appropriate treatment, and/or making a referral for testing or a consultation. The physician can also easily fill in key pieces of missing information (e.g., blood pressure, weight, or smoking status) that have been flagged by the system.
  • Patient review: The forms include a section that summarizes the patient's health status, care that has been given, and remaining care needs. The patient and physician review this information together to decide on future care needs and options.
  • Patient education and materials: Physicians check off needed educational materials based on the patient's needs. The physician clicks one button to print all information needed by the patient, including laboratory order forms, new prescriptions, and educational materials.

Context of the Innovation

The Partners HealthCare System is an integrated system that includes Brigham and Women's Hospital, Massachusetts General Hospital, several community hospitals, and Partners Community HealthCare, a physician network of more than 4,000 clinicians who use Partners’ internally developed EHR. Approximately 20 percent of Partners’ patients have CAD and/or diabetes, and each year approximately 22,000 patients with an acute respiratory infection visit primary care practices affiliated with Brigham and Women's Hospital and Massachusetts General Hospital. Given the meaningful volume of patients affected by these conditions, three physicians—Blackford Middleton, MD; Jeffrey Schnipper, MD; and Jeffrey Linder, MD—decided to spearhead the development of the Smart Forms, believing that adding point-of-care decision support functionality to the EHR could enhance its impact (and hence improve quality of care) by making it easy for physicians to use within their current workflow.

Did It Work?

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Results

The system improved the appropriateness of antibiotic prescribing for acute respiratory infections and increased use of recommended therapies and improved documentation for patients with CAD and diabetes.
  • More appropriate antibiotic prescribing: A recent randomized controlled trial (RCT) (the results of which were provided in June 2012) found that, when used, the system reduced the inappropriate prescribing of antibiotics for acute respiratory infections.4 Previously, a 4-week pilot study involving 10 primary care physicians and 26 patients with respiratory symptoms found that clinicians using the Smart Form prescribed antibiotics appropriately to all patients (i.e., according to the guidelines), while those not using the form prescribed them appropriately only 90 percent of the time. During the previous cold and flu season, those using the Smart Form inappropriately prescribed antibiotics to patients who did not need them 15 percent of the time, below the 21 percent rate when not using the form.1
  • Greater adherence to—and documentation of—recommended diabetes and CAD care: A recent RCT (the results of which were provided in June 2012) found that the patients of physicians using the Smart Form were more likely to get care deficiencies addressed than those treated by physicians not using the form; improvements stemmed both from better documentation (e.g., of smoking status) and greater adherence to recommended care (e.g., prescription of antiplatelet agents when appropriate).5 Previously, a pilot study involving 30 physicians and 1,940 patients with CAD and/or diabetes found that clinicians addressed an average of 13.9 percent of care deficiencies during the 6 weeks after they began using the Smart Form, well above the 8.6 percent rate during the 6 weeks before implementation. In addition, these deficiencies were generally corrected within a month of the visit in which the Smart Form was used.6 Specific results are as follows:
    • More likely to change therapy for those with high blood glucose: Physicians changed or increased therapy for 16.9 percent of patients currently on diabetes medications who had a hemoglobin A1c level above 7 percent, compared with just 9.8 percent of such patients before implementation.
    • More appropriate beta blocker use and documentation: Physicians documented beta blocker use or a contraindication in 100 percent of cases after implementation, compared with none before implementation.
  • Better documentation of health status information: Among those patients without appropriate documentation in the previous 12 months, 93.3 percent had their most recent blood pressure reading documented properly in the EHR after implementation, compared with just 26.7 percent before. Similar improvements were seen for height and weight (9.4 percent after implementation, compared with 4.6 percent before) and smoking status (23.9 percent, 5.2 percent).

Evidence Rating (What is this?)

Strong: The evidence consists of RCTs and pre- and post-implementation comparisons of key outcomes measures, including the proportion of care deficiencies addressed, rates of appropriate antibiotic prescribing, adjustments in diabetes therapy, prescribing of antiplatelet therapy and beta blocker medications, and documentation of key metrics in the EHR.

How They Did It

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Planning and Development Process

Key steps included the following:
  • Background research: The physician investigators performed background research to identify appropriate care and existing guidelines for the targeted conditions.
  • Focus groups: The investigators held approximately four focus groups of six to eight primary care physicians to discuss their use of Partners' EHR and determine their attitudes toward electronic decision support. Participants discussed the challenges inherent in CAD and diabetes management and appropriate antibiotic prescribing, along with potential barriers to use of a clinical decision support tool.
  • Paper-based prototypes: The investigators developed paper-based prototypes of the forms and showed them to colleagues to get initial feedback. They revised the prototypes iteratively over a year-long period.
  • Computerized prototypes: The information technology department developed computer prototypes based on the paper forms. The investigators showed the computerized forms to colleagues to get their feedback.
  • Usability testing and feedback: The investigators conducted standardized usability testing using simulated patient cases. Physician “testers” used the forms to simulate the provision of care. Investigators recorded the number of keystrokes, videotaped users to gauge their reactions to the system, and evaluated their clinical actions and the quality of documentation.
  • Physician training: Pilot test and RCT participants viewed a 10-minute online demonstration of the system, and participated in group and one-on-one training sessions on its use.
  • Pilot testing, with additional feedback: As noted, each form was evaluated in a pilot test. During these tests, investigators sought additional feedback on usability and the quality of the decision support logic and clinical recommendations. A feedback button allowed users to provide real-time comments. An outside consulting group also conducted one-on-one interviews with users to get their impressions. The investigators used this feedback to improve the system's usability and clinical content.
  • RCT testing and rollout: Each form is being evaluated in an RCT that includes 10 primary care physician practices. Partners' leaders have not yet decided whether to expand the program to all physicians.

Resources Used and Skills Needed

  • Staffing: The program requires no new staff; existing staff incorporate use of the system into their daily activities.
  • Costs: Total development costs are estimated at approximately $9 million.
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Funding Sources

Agency for Healthcare Research and Quality; Partners Healthcare System
A 5-year, $1.5 million grant from the Agency for Healthcare Research and Quality funded a portion of the research and development costs. Partners contributed the remaining $7.5 million to fund the labor costs associated with system and software development, design, training, and testing.end fs

Tools and Other Resources

The guidelines that inform the CAD/diabetes Smart Form can be found in:
  • JNC 7 Complete Report: The Science Behind the New Guidelines. The Seventh Report of the Joint National Committee on Prevention, Detection, Evaluation, and Treatment of High Blood Pressure. National Heart Lung and Blood Institute. 2004. Available at: http://www.nhlbi.nih.gov/guidelines/hypertension/jnc7full.htm.
  • Third Report of the National Cholesterol Education Program (NCEP) Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults (Adult Treatment Panel III) final report and 2004 Update. Available at: http://www.nhlbi.nih.gov/guidelines/cholesterol/index.htm.
  • American Diabetes Association. Standards of medical care in diabetes—2007. Diabetes Care. 2007;30 Suppl 1:S4-S41. [PubMed]
  • Smith SC, Jr., Allen J, Blair SN, et al. AHA/ACC guidelines for secondary prevention for patients with coronary and other atherosclerotic vascular disease: 2006 update: endorsed by the National Heart, Lung, and Blood Institute. Circulation. 2006;113(19):2363-72. [PubMed]
  • A clinical practice guideline for treating tobacco use and dependence: a US Public Health Service report. The Tobacco Use and Dependence Clinical Practice Guideline Panel, Staff, and Consortium Representatives. JAMA. 2000;283(24):3244-54. [PubMed]
  • Murray EW. Smoking cessation clinical practice guideline update and Agency for Healthcare Research and Quality tobacco resources. Tob Control. 2000;9 Suppl 1:I72-3. [PubMed]
  • National Heart, Lung, and Blood Institute. Executive summary of the clinical guidelines on the identification, evaluation, and treatment of overweight and obesity in adults. Arch Intern Med. 1998;158(17):1855-67. [PubMed]
  • Leon AS, Franklin BA, Costa F, et al. AHA Scientific Statement: Cardiac Rehabilitation and Secondary Prevention of Coronary Heart Disease. Circulation. 2005;111(3):369-76. [PubMed]

Adoption Considerations

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Getting Started with This Innovation

  • Focus on usability: Decision support must be easily accessible and user-friendly, offering information at the right point in the clinician’s decisionmaking process. One of the recent RCTs found that overall use of the coronary artery disease and diabetes system remains low.5
  • Solicit clinician feedback: Seek physician feedback at all stages of development to ensure that the tool will be valuable to—and thus used by—physicians. Physician feedback should relate to the usability and clinical content of the system.
  • Allow physicians to provide training if possible: Physicians will be more likely to accept training and view the system as valuable if such training comes from a peer (rather than an analyst).
  • Expect initial resistance to changing work processes: Clinicians become acclimated to providing and documenting care in a certain way. Their willingness to adopt the new system will depend on their satisfaction with the current one, their willingness to change, and the perceived benefits of the new system (to the clinicians and patients). Incorporating clinically relevant decision support into the clinician’s existing workflow will make adoption more likely.
  • Build intervention into workflow: For example, the form should automatically replace the one currently used by clinicians and should allow the user to conduct other activities without impediment (added June 2012).
  • Ensure that system supports team-based care: For example, it should allow some information to be added by nurses or other healthcare personnel, while physicians approve the final version of the form (added June 2012).

Sustaining This Innovation

  • Allow for customization whenever possible: Users appreciate the ability to customize applications to their own work processes and are more likely to adopt and continue using the system if such capabilities exist.
  • Engage patients in the technology: Showing the computer screen to the patient and collaboratively writing notes can enhance the patient–physician interaction, thus making physicians more likely to continue using the system. To maintain patient support for the system, make it clear that care recommendations are based on individual health needs (rather than being standardized recommendations automatically generated by the computer).
  • Consider role of health care financing and organization: Sustained, increased use of this type of tool may require changes to the organization and financing of health care, such as the Patient-Centered Medical Home demonstration program that includes financial incentives for care activities outside of a patient visit and multidisciplinary teams (added June 2012).

Additional Considerations

While initial studies show that such systems have promise when used, future research should focus on refining the tools to improve usability, integrating them into current EHR platforms, and promoting their use, perhaps through organizational changes to primary care practices.

More Information

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References/Related Articles

(added June 2012) Schnipper JL, Linder JA, Palchuk MB, et al. Effects of documentation-based decision support on chronic disease management. Am J Manag Care. Dec 2010;16(12 Suppl HIT):SP72-81. [PubMed]

(added June 2012) Linder JA, Schnipper JL, Tsurikova R, et al. Documentation-based clinical decision support to improve antibiotic prescribing for acute respiratory infections in primary care: a cluster randomised controlled trial. Inform Prim Care. 2010;17(4):231-240. [PubMed]

(added June 2012) Linder JA, Schnipper JL, Tsurikova R, et al. Self-reported familiarity with acute respiratory infection guidelines and antibiotic prescribing in primary care. Int J Qual Health Care. Dec 2010;22(6):469-475. [PubMed]

(added June 2012) Linder JA, Schnipper JL, Tsurikova R et al. Electronic health record feedback to improve antibiotic prescribing for acute respiratory infections. Am J Managed Care. 2010;16(12 Spec No):e311-e319. [PubMed]

Schnipper JL, Linder JA, Palchuk MB, et al. “Smart Forms” in an electronic medical record: documentation-based clinical decision support to improve disease management. J Am Med Inform Assoc. 2008;15(4):513-23. [PubMed]

Linder JA, Schnipper JL, Volk LA, et al. Clinical decision support to improve antibiotic prescribing for acute respiratory infections: results of a pilot study. AMIA Annu Symp Proc. 2007;468-72. [PubMed]

Schnipper JL, McColgan KE, Linder JA, et al. Improving management of chronic diseases with documentation-based clinical decision support: results of a pilot study. AMIA Annu Symp Proc. 2008;1050. [PubMed]

Linder JA, Rose AF, Palchuk MB, et al. Decision support for acute problems: the role of the standardized patient in usability testing. J Biomed Inform. 2006;39:648-55. [PubMed]

Footnotes

1 Linder JA, Schnipper JL, Volk LA, et al. Clinical decision support to improve antibiotic prescribing for acute respiratory infections: results of a pilot study. AMIA Annu Symp Proc. 2007;468-72. [PubMed]
2 Parchman ML, Romero RL, Pugh JA. Encounters by patients with type 2 diabetes—complex and demanding: an observational study. Ann Fam Med. 2006;4(1):40-5. [PubMed]
3 Schnipper JL, Linder JA, Palchuk MB, et al. “Smart Forms” in an electronic medical record: documentation-based clinical decision support to improve disease management. J Am Med Inform Assoc. 2008;15(4):513-23. [PubMed]
4 Linder JA, Schnipper JL, Tsurikova R, et al. Electronic health record feedback to improve antibiotic prescribing for acute respiratory infections. Am J Managed Care. 2010;16(12 Spec No):e311-e319. [PubMed]
5 Schnipper JL, Linder JA, Palchuk MB, et al. Effects of documentation-based decision support on chronic disease management. Am J Managed Care. Dec 2010;16(12 Suppl HIT):SP72-81. [PubMed]
6 Schnipper JL, McColgan KE, Linder JA, et al. Improving management of chronic diseases with documentation-based clinical decision support: results of a pilot study. AMIA Annu Symp Proc. 2008;1050. [PubMed]
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Disclaimer: The inclusion of an innovation in the Innovations Exchange does not constitute or imply an endorsement by the U.S. Department of Health and Human Services, the Agency for Healthcare Research and Quality, or Westat of the innovation or of the submitter or developer of the innovation. Read more.

Original publication: August 05, 2009.
Original publication indicates the date the profile was first posted to the Innovations Exchange.

Last updated: November 06, 2013.
Last updated indicates the date the most recent changes to the profile were posted to the Innovations Exchange.

Date verified by innovator: October 09, 2013.
Date verified by innovator indicates the most recent date the innovator provided feedback during the annual review process. The innovator is invited to review, update, and verify the profile annually.

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