William T. Lester

How Doctors Choose Medications to Treat Type 2 Diabetes A national survey of specialists and academic generalists

OBJECTIVE—Glycemic control remains suboptimal despite the wide range of available medications. More effective medication prescription might result in better control. However, the process by which physicians choose glucose-lowering medicines is poorly understood. We sought to study the means by which physicians choose medications for type 2 diabetic patients. RESEARCH DESIGN AND METHODS—We surveyed 886 physician members of either the Society of General Internal Medicine (academic generalists, response rate 30%) or the American Diabetes Association (specialists, response rate 23%) currently managing patients with type 2 diabetes. Respondents weighed the importance of 15 patient, physician, and nonclinical factors when deciding which medications to prescribe for type 2 diabetic subjects at each of three management stages (initiation, use of second-line oral agents, and insulin). RESULTS—Respondents reported using a median of five major considerations (interquartile range 4–6) at each stage. Frequently cited major considerations included overall assessment of the patients health/comorbidity, A1C level, and patients adherence behavior but not expert guidelines/hospital algorithms or patient age. For insulin initiation, academic generalists placed greater emphasis on patient adherence (76 vs. 60% of specialists, P < 0.001). These generalists also identified patient fear of injections (68%) and patient desire to prolong noninsulin therapy (68%) as major insulin barriers. Overall, qualitative factors (e.g., adherence, motivation, overall health assessment) were somewhat more highly considered than quantitative factors (e.g., A1C, age, weight) with mean aggregate scores of 7.3 vs. 6.9 on a scale of 0–10, P < 0.001. CONCLUSIONS—The physicians in our survey considered a wide range of qualitative and quantitative factors when making medication choices for hyperglycemia management. The apparent complexity of the medication choice process contrasts with current evidence-based treatment guidelines.

Randomized controlled trial of an informatics-based intervention to increase statin prescription for secondary prevention of coronary disease

OBJECTIVE: Suboptimal treatment of hyperlipidemia in patients with coronary artery disease (CAD) is well documented. We report the impact of a computer-assisted physician-directed intervention to improve secondary prevention of hyperlipidemia.DESIGN AND SETTING: Two hundred thirty-five patients under the care of 14 primary care physicians in an academically affiliated practice with an electronic health record were enrolled in this proof-of-concept physician-blinded randomized, controlled trial. Each patient with CAD or risk equivalent above National Cholesterol Education Program-recommended low-density lipoprotein (LDL) treatment goal for greater than 6 months was randomized, stratified by physician and baseline LDL. Physicians received a single e-mail per intervention patient. E-mails were visit independent, provided decision support, and facilitated “one-click” order writing.MEASUREMENTS: The primary outcomes were changes in hyperlipidemia prescriptions, time to prescription change, and changes in LDL levels. The time spent using the system was assessed among intervention patients.RESULTS: A greater proportion of intervention patients had prescription changes at 1 month (15.3% vs 2%, P=.001) and 1 year (24.6% vs 17.1%, P=.14). The median interval to first medication adjustment occurred earlier among intervention patients (0 vs 7.1 months, P=.005). Among patients with baseline LDLs >130 mg/dL, the first postintervention LDLs were substantially lower in the intervention group (119.0 vs 138.0 mg/dL, P=.04). Physician processing time was under 60 seconds per e-mail.CONCLUSION: A visit-independent disease management tool resulted in significant improvement in secondary prevention of hyperlipidemia at 1-month postintervention and showed a trend toward improvement at 1 year.

Lessons from Implementing a Combined Workflow–Informatics System for Diabetes Management

Shortcomings surrounding the care of patients with diabetes have been attributed largely to a fragmented, disorganized, and duplicative health care system that focuses more on acute conditions and complications than on managing chronic disease. To address these shortcomings, we developed a diabetes registry population management application to change the way our staff manages patients with diabetes. Use of this new application has helped us coordinate the responsibilities for intervening and monitoring patients in the registry among different users. Our experiences using this combined workflow-informatics intervention system suggest that integrating a chronic disease registry into clinical workflow for the treatment of chronic conditions creates a useful and efficient tool for managing disease.

A Clinical Database-Driven Approach to Decision Support: Predicting Mortality Among Patients with Acute Kidney Injury.

In exploring an approach to decision support based on information extracted from a clinical database, we developed mortality prediction models of intensive care unit (ICU) patients who had acute kidney injury (AKI) and compared them against the Simplified Acute Physiology Score (SAPS). We used MIMIC, a public de-identified database of ICU patients admitted to Beth Israel Deaconess Medical Center, and identified 1400 patients with an ICD9 diagnosis of AKI and who had an ICU stay > 3 days. Multivariate regression models were built using the SAPS variables from the first 72 hours of ICU admission. All the models developed on the training set performed better than SAPS (AUC = 0.64, Hosmer-Lemeshow p < 0.001) on an unseen test set; the best model had an AUC = 0.74 and Hosmer-Lemeshow p = 0.53. These findings suggest that local customized modeling might provide more accurate predictions. This could be the first step towards an envisioned individualized point-of-care probabilistic modeling using ones clinical database.

Efficacy of a Clinical Decision-Support System in an HIV Practice: A Randomized Trial

BACKGROUND Data to support improved patient outcomes from clinical decision-support systems (CDSSs) are lacking in HIV care. OBJECTIVE To test the efficacy of a CDSS in improving HIV outcomes in an outpatient clinic. DESIGN Randomized, controlled trial. (ClinicalTrials.gov registration number: NCT00678600) SETTING Massachusetts General Hospital HIV Clinic. PARTICIPANTS HIV care providers and their patients. INTERVENTION Computer alerts were generated for virologic failure (HIV RNA level >400 copies/mL after a previous HIV RNA level ≤400 copies/mL), evidence of suboptimal follow-up, and 11 abnormal laboratory test results. Providers received interactive computer alerts, facilitating appointment rescheduling and repeated laboratory testing, for half of their patients and static alerts for the other half. MEASUREMENTS The primary end point was change in CD4 cell count. Other end points included time to clinical event, 6-month suboptimal follow-up, and severe laboratory toxicity. RESULTS Thirty-three HIV care providers followed 1011 patients with HIV. In the intervention group, the mean increase in CD4 cell count was greater (0.0053 vs. 0.0032 × 109 cells/L per month; difference, 0.0021 × 109 cells/L per month [95% CI, 0.0001 to 0.004]; P = 0.040) and the rate of 6-month suboptimal follow-up was lower (20.6 vs. 30.1 events per 100 patient-years; P = 0.022) than those in the control group. Median time to next scheduled appointment was shorter in the intervention group than in the control group after a suboptimal follow-up alert (1.71 vs. 3.48 months; P < 0.001) and after a toxicity alert (2.79 vs. >6 months; P = 0.072). More than 90% of providers supported adopting the CDSS as part of standard care. LIMITATION This was a 1-year informatics study conducted at a single hospital subspecialty clinic. CONCLUSION A CDSS using interactive provider alerts improved CD4 cell counts and clinic follow-up for patients with HIV. Wider implementation of such systems can provide important clinical benefits. PRIMARY FUNDING SOURCE National Institute of Allergy and Infectious Diseases.

Mammography FastTrack: An Intervention to Facilitate Reminders for Breast Cancer Screening across a Heterogeneous Multi-clinic Primary Care Network

Health care information technology can be a means to improve quality and efficiency in the primary care setting. However, merely applying technology without addressing how it fits into provider workflow and existing systems is unlikely to achieve improvement goals. Improving quality of primary care, such as cancer screening rates, requires addressing barriers at system, provider, and patient levels. The authors report the development, implementation, and preliminary use of a new breast cancer screening outreach program in a large multicenter primary care network. This installation paired population-based surveillance with customized information delivery based on a validated model linking patients to providers and practices. In the first six months, 86% of physicians and all case managers voluntarily participated in the program. Providers intervened in 83% of the mammogram-overdue population by initiating mailed reminders or deferring contact. Overall, 63% of patients were successfully contacted. Systematic population-based efforts are promising tools to improve preventative care.

Genetic testing behavior and reporting patterns in electronic medical records for physicians trained in a primary care specialty or subspecialty.

OBJECTIVE To characterize important patterns of genetic testing behavior and reporting in modern electronic medical records (EMRs) at the institutional level. MATERIALS AND METHODS Retrospective observational study using EMR data of all 10,715 patients who received genetic testing by physicians trained in a primary care specialty or subspecialty at an academic medical center between January 1, 2008 and December 31, 2010. RESULTS Patients had a mean±SD age of 38.3±15.8 years (median 36.1, IQR 30.0-43.8). The proportion of female subjects in the study population was larger than in the general patient population (77.2% vs 55.0%, p<0.001) and they were younger than the male subjects in the study (36.5±13.2 vs 44.6±21.2 years, p<0.001). Approximately 1.1% of all patients received genetic testing. There were 942 physicians who ordered a total of 15,320 genetic tests. By volume, commonly tested genes involved mutations for cystic fibrosis (36.7%), prothrombin (13.7%), Tay-Sachs disease (6.7%), hereditary hemochromatosis (4.4%), and chronic myelogenous leukemia (4.1%). EMRs stored reports as free text with categorical descriptions of mutations and an average length of 269.4±153.2 words (median 242, IQR 146-401). CONCLUSIONS In this study, genetic tests were often ordered by a diverse group of physicians for women of childbearing age being evaluated for diseases that may affect potential offspring. EMRs currently serve primarily as a storage warehouse for textual reports that could potentially be transformed into meaningful structured data for next-generation clinical decision support. Further studies are needed to address the design, development, and implementation of EMRs capable of managing the critical genetic health information challenges of the future.

A Neural Network Approach to Electromyographic Signal Processing for a Motor Control Task

A hybrid modeling structure composed of a one degree of freedom computational musculoskeletal model and a feedforward multi-layer perceptron neural network was used to effectively map electromyography (EMG) from a human exercise trial to muscle activations in a physiologically feasible and accurate fashion. Several configurations of the complete hybrid system were used to map four muscle surface EMGs from a ballistic elbow flexion to normalized muscle activations, estimated individual muscle forces and torque about the joint. The net joint torque was used to train the neural portion of the hybrid system to minimize kinematic error. The model allowed the estimation of the nonobservable parameters: normalized muscle activations and forces which was used to penalize the learning system. With these parameters in the learning equation, our system produced muscle activations consistent with the classic triphasic response present in ballistic movements.

New models of population management for patients with diabetes - using informatics tools to support primary care

Diabetes management continues to fall short of evidence-based goals of care. Population management represents a new approach to diabetes care for large numbers of patients with diabetes cared for within a single clinical system. This method is information intensive and generally requires an advanced informatics infrastructure. While Information Processing is a critical first step in population management, to have a significant impact on disease control population-based intervention must also employ potent Clinical Action tools that lower barriers to effective care. In this review we present two recent population management interventions within our health system that illustrate the principles of Information Processing and Clinical Action in diabetes care.

Diabetes Information Technology: Designing Informatics Systems to Catalyze Change in Clinical Care

Current computerized reminder and decision support systems intended to improve diabetes care have had a limited effect on clinical outcomes. Increasing pressures on health care networks to meet standards of diabetes care have created an environment where information technology systems for diabetes management are often created under duress, appended to existing clinical systems, and poorly integrated into the existing workflow. After defining the components of diabetes disease management, the authors present an eight-step conceptual framework to guide the development of more effective diabetes information technology systems for translating clinical information into clinical action.