Comparison of Computer-based Clinical Decision Support Systems and Content for Diabetes Mellitus

Promoting Judicious Primary Care Referral of Patients with Chest Pain to Cardiology: A Quality Improvement Initiative

OBJECTIVE

To decrease referrals to cardiology of patients ages 7 to 21 years with low-probability cardiac pathology who presented to primary care with chest pain by 50% within 24 months.

STUDY DESIGN

A multidisciplinary team designed and implemented an initiative consisting of 1) a decision support tool (DST), 2) educational sessions, 3) routine feedback to improve use of referral criteria, and 4) patient family education. Four pediatric practices, comprising 34 pediatricians and 7 nurse practitioners, were included in this study. We tracked progress via statistical process control charts.

RESULTS

A total of 421 patients ages 7 to 21 years presented with chest pain to their pediatrician. The utilization of the DST increased from baseline of 16% to 68%. Concurrently, the percentage of low-probability cardiology referrals in pediatric patients ages 7 to 21 years who presented with chest pain decreased from 17% to 5% after our interventions. At a median follow-up time of 0.9 years (interquartile range, 0.3-1.6 years), no patient had a life-threatening cardiac event.

CONCLUSION

Our health care improvement initiative to reduce low-probability cardiology referrals for children presenting to primary care practices with chest pain was feasible, effective, and safe.

Clinician Perceptions of a Computerized Decision Support System for Pediatric Type 2 Diabetes Screening

OBJECTIVE

With the increasing prevalence of type 2 diabetes (T2D) in youth, primary care providers must identify patients at high risk and implement evidence-based screening promptly. Clinical decision support systems (CDSSs) provide clinicians with personalized reminders according to best evidence. One example is the Child Health Improvement through Computer Automation (CHICA) system, which, as we have previously shown, significantly improves screening for T2D. Given that the long-term success of any CDSS depends on its acceptability and its users' perceptions, we examined what clinicians think of the CHICA diabetes module.

METHODS

CHICA users completed an annual quality improvement and satisfaction questionnaire. Between May and August of 2015 and 2016, the survey included two statements related to the T2D-module: (1) "CHICA improves my ability to identify patients who might benefit from screening for T2D" and (2) "CHICA makes it easier to get the lab tests necessary to identify patients who have diabetes or prediabetes." Answers were scored using a 5-point Likert scale and were later converted to a 2-point scale: agree and disagree. The Pearson chi-square test was used to assess the relationship between responses and the respondents. Answers per cohort were compared using the Mann-Whitney U-test.

RESULTS

The majority of respondents (N = 60) agreed that CHICA improved their ability to identify patients who might benefit from screening but disagreed as to whether it helped them get the necessary laboratories. Scores were comparable across both years.

CONCLUSION

CHICA was endorsed as being effective for T2D screening. Research is needed to improve satisfaction for getting laboratories with CHICA.

Decision-Centered Design of Patient Information Visualizations to Support Chronic Pain Care

BACKGROUND

For complex patients with chronic conditions, electronic health records (EHRs) contain large amounts of relevant historical patient data. To use this information effectively, clinicians may benefit from visual information displays that organize and help them make sense of information on past and current treatments, outcomes, and new treatment options. Unfortunately, few clinical decision support tools are designed to support clinical sensemaking.

OBJECTIVE

The objective of this study was to describe a decision-centered design process, and resultant interactive patient information displays, to support key clinical decision requirements in chronic noncancer pain care.

METHODS

To identify key clinical decision requirements, we conducted critical decision method interviews with 10 adult primary care clinicians. Next, to identify key information needs and decision support design seeds, we conducted a half-day multidisciplinary design workshop. Finally, we designed an interactive prototype to support the key clinical decision requirements and information needs uncovered during the previous research activities.

RESULTS

The resulting Chronic Pain Treatment Tracker prototype summarizes the current treatment plan, past treatment history, potential future treatments, and treatment options to be cautious about. Clinicians can access additional details about each treatment, current or past, through modal views. Additional decision support for potential future treatments and treatments to be cautious about is also provided through modal views.

CONCLUSION

This study designed the Chronic Pain Treatment Tracker, a novel approach to decision support that presents clinicians with the information they need in a structure that promotes quick uptake, understanding, and action.

Comparing Real-Time Self-Tracking and Device-Recorded Exercise Data in Subjects with Type 1 Diabetes

BACKGROUND

Insulin therapy, medical nutrition therapy, and physical activity are required for the treatment of type 1 diabetes (T1D). There is a lack of studies in real-life environments that characterize patient-reported data from logs, activity trackers, and medical devices (e.g., glucose sensors) in the context of exercise.

OBJECTIVE

The objective of this study was to compare data from continuous glucose monitor (CGM), wristband heart rate monitor (WHRM), and self-tracking with a smartphone application (app), iDECIDE, with regards to exercise behaviors and rate of change in glucose levels.

METHODS

Participants with T1D on insulin pump therapy tracked exercise for 1 month with the smartphone app while WHRM and CGM recorded data in real time. Exercise behaviors tracked with the app were compared against WHRM. The rate of change in glucose levels, as recorded by CGM, resulting from exercise was compared between exercise events documented with the app and recorded by the WHRM.

RESULTS

Twelve participants generated 277 exercise events. Tracking with the app aligned well with WHRM with respect to frequency, 3.0 (2.1) and 2.5 (1.8) days per week, respectively (p = 0.60). Duration had very high agreement, the mean duration from the app was 65.6 (55.2) and 64.8 (54.9) minutes from WHRM (p = 0.45). Intensity had a low concordance between the data sources (Cohen's kappa = 0.2). The mean rate of change of glucose during exercise was -0.27 mg/(dL*min) and was not significantly different between data sources or intensity (p = 0.21).

CONCLUSION

We collated and analyzed data from three heterogeneous sources from free-living participants. Patients' perceived intensity of exercise can serve as a surrogate for exercise tracked by a WHRM when considering the glycemic impact of exercise on self-care regimens.

Development of a clinical decision support system for diabetes care: A pilot study

Management of complex chronic diseases such as diabetes requires the assimilation and interpretation of multiple laboratory test results. Traditional electronic health records tend to display laboratory results in a piecemeal and segregated fashion. This makes the assembly and interpretation of results related to diabetes care challenging. We developed a diabetes-specific clinical decision support system (Diabetes Dashboard) interface for displaying glycemic, lipid and renal function results, in an integrated form with decision support capabilities, based on local clinical practice guidelines. The clinical decision support system included a dashboard feature that graphically summarized all relevant laboratory results and displayed them in a color-coded system that allowed quick interpretation of the metabolic control of the patients. An alert module informs the user of tests that are due for repeat testing. An interactive graph module was also developed for better visual appreciation of the trends of the laboratory results of the patient. In a pilot study involving case scenarios administered via an electronic questionnaire, the Diabetes Dashboard, compared to the existing laboratory reporting interface, significantly improved the identification of abnormal laboratory results, of the long-term trend of the laboratory tests and of tests due for repeat testing. However, the Diabetes Dashboard did not significantly improve the identification of patients requiring treatment adjustment or the amount of time spent on each case scenario. In conclusion, we have developed and shown that the use of the Diabetes Dashboard, which incorporates several decision support features, can improve the management of diabetes. It is anticipated that this dashboard will be most helpful when deployed in an outpatient setting, where physicians can quickly make clinical decisions based on summarized information and be alerted to pertinent areas of care that require additional attention.

Testing electronic health records in the "production" environment: an essential step in the journey to a safe and effective health care system

Thorough and ongoing testing of electronic health records (EHRs) is key to ensuring their safety and effectiveness. Many health care organizations limit testing to test environments separate from, and often different than, the production environment used by clinicians. Because EHRs are complex hardware and software systems that often interact with other hardware and software systems, no test environment can exactly mimic how the production environment will behave. An effective testing process must integrate safely conducted testing in the production environment itself, using test patients. We propose recommendations for how to safely incorporate testing in production into current EHR testing practices, with suggestions regarding the incremental release of upgrades, test patients, tester accounts, downstream personnel, and reporting.

A Framework for Usable and Effective Clinical Decision Support: Experience from the iCPR Randomized Clinical Trial

INTRODUCTION

The promise of Clinical Decision Support (CDS) has always been to transform patient care and improve patient outcomes through the delivery of timely and appropriate recommendations that are patient specific and, more often than not, are appropriately actionable. However, the users of CDS-providers-are frequently bombarded with inappropriate and inapplicable CDS that often are not informational, not integrated into the workflow, not patient specific, and that may present out of date and irrelevant recommendations.

METHODS

The integrated clinical prediction rule (iCPR) project was a randomized clinical trial (RCT) conducted to determine if a novel form of CDS, i.e., clinical prediction rules (CPRs), could be efficiently integrated into workflow and result in changes in outcomes (e.g., antibiotic ordering) when embedded within a commercial electronic health record (EHR). We use the lessons learned from the iCPR project to illustrate a framework for constructing usable, useful, and effective actionable CDS while employing off-the-shelf functionality in a production system. Innovations that make up the framework combine the following: (1) active and actionable decision support, (2) multiple rounds of usability testing with iterative development for user acceptance, (3) numerous context sensitive triggers, (4) dedicated training and support for users of the CDS tool for user adoption, and (5) support from clinical and administrative leadership. We define "context sensitive triggers" as being workflow events (i.e., context) that result in a CDS intervention.

DISCUSSION

Success of the framework can be measured by CDS adoption (i.e., intervention is being used), acceptance (compliance with recommendations), and clinical outcomes (where appropriate). This framework may have broader implications for the deployment of Health Information Technology (HIT).

RESULTS AND CONCLUSION

iCPR was well adopted(57.4% of users) and accepted (42.7% of users). Usability testing identified and fixed many issues before the iCPR RCT. The level of leadership support and clinical guidance for iCPR was key in establishing a culture of acceptance for both the tool and its recommendations contributing to adoption and acceptance. The dedicated training and support lead to the majority of the residents reporting a high level of comfort with both iCPR tools strep pharyngitis (64.4 percent) and pneumonia (62.7 percent) as well as a high likelihood of using the tools in the future. A surprising framework addition resulted from usability testing: context sensitive triggers.

Comparative outcome studies of clinical decision support software: limitations to the practice of evidence-based system acquisition

Clinical decision support systems (CDSSs) assist clinicians with patient diagnosis and treatment. However, inadequate attention has been paid to the process of selecting and buying systems. The diversity of CDSSs, coupled with research obstacles, marketplace limitations, and legal impediments, has thwarted comparative outcome studies and reduced the availability of reliable information and advice for purchasers. We review these limitations and recommend several comparative studies, which were conducted in phases; studies conducted in phases and focused on limited outcomes of safety, efficacy, and implementation in varied clinical settings. Additionally, we recommend the increased availability of guidance tools to assist purchasers with evidence-based purchases. Transparency is necessary in purchasers' reporting of system defects and vendors' disclosure of marketing conflicts of interest to support methodologically sound studies. Taken together, these measures can foster the evolution of evidence-based tools that, in turn, will enable and empower system purchasers to make wise choices and improve the care of patients.

Opportunities to Increase the Effectiveness of EHR-Based Diabetes Clinical Decision Support

There are many opportunities to improve diabetes care through more effective use of EHR-based CDS. The report of Kantor et al. [16] is encouraging because it demonstrates sustained efforts by leading health care organizations to implement diabetes-related EHR-based CDS. However, lack of sophisticated treatment-specific CDS and lack of prioritized recommendations are a cause for concern. Even more disturbing is the substantive heterogeneity in content of diabetes CDS recommendations now in the field. Some of CDS recommendations described by Kantor et al. [16] are clearly not evidence-based and could increase costs while not improving clinical benefits. The timely identification of these problems is an awkward but necessary first step towards improvement. The health care organizations that are pioneers in the field should be congratulated and encouraged to continue their collaborative efforts to increase the efficiency and effectiveness of EHR-based CDS. Attending to the modest proposals put forward here and by others may help translate the massive investments that we have made in EHR technology into clinical benefits for our patients.