Designing a Multilevel Strategy to Enhance Secondary Prevention of Heart Disease.

Background Coronary artery calcification (CAC), the presence and severity of which strongly predict underlying coronary artery disease (CAD), can be seen on dedicated cardiac imaging studies or incidentally on noncardiac ones; however, the latter findings are commonly managed by primary care clinicians without clear accompanying recommendations and may represent an underrecognized opportunity to optimize secondary prevention of CAD. Methods Standardized practice guidelines and a multilevel implementation strategy for improving secondary prevention of cardiovascular disease through incidentally identified CAC were developed by an interdisciplinary committee. Evidence-based implementation strategies were selected1 and included integrating practice guidelines into radiology reports within the electronic medical records. Outpatient noncardiac computerized tomography scans performed before and after this initiative were retrospectively reviewed to evaluate changes in statin prescribing. Results Authors demonstrated an increase in the percentage of patients with mild CAC prescribed a statin and an increase in the percentage of patients with severe CAC prescribed a high-intensity statin after implementation of standardized practice guidelines and evidence-based implementation strategies. Conclusion Incidental CAC identification is common, particularly in those without known CAD. A multilevel implementation strategy and use of standardized practice guidelines appeared to improve provider prescribing behavior in the primary care setting and may provide an opportunity to enhance secondary CAC prevention.

Personalized treatment options for chronic diseases using precision cohort analytics.

To support point-of-care decision making by presenting outcomes of past treatment choices for cohorts of similar patients based on observational data from electronic health records (EHRs), a machine-learning precision cohort treatment option (PCTO) workflow consisting of (1) data extraction, (2) similarity model training, (3) precision cohort identification, and (4) treatment options analysis was developed. The similarity model is used to dynamically create a cohort of similar patients, to inform clinical decisions about an individual patient. The workflow was implemented using EHR data from a large health care provider for three different highly prevalent chronic diseases: hypertension (HTN), type 2 diabetes mellitus (T2DM), and hyperlipidemia (HL). A retrospective analysis demonstrated that treatment options with better outcomes were available for a majority of cases (75%, 74%, 85% for HTN, T2DM, HL, respectively). The models for HTN and T2DM were deployed in a pilot study with primary care physicians using it during clinic visits. A novel data-analytic workflow was developed to create patient-similarity models that dynamically generate personalized treatment insights at the point-of-care. By leveraging both knowledge-driven treatment guidelines and data-driven EHR data, physicians can incorporate real-world evidence in their medical decision-making process when considering treatment options for individual patients.

Precision population analytics: population management at the point-of-care.

OBJECTIVE: To present clinicians at the point-of-care with real-world data on the effectiveness of various treatment options in a precision cohort of patients closely matched to the index patient. MATERIALS AND METHODS: We developed disease-specific, machine-learning, patient-similarity models for hypertension (HTN), type II diabetes mellitus (T2DM), and hyperlipidemia (HL) using data on approximately 2.5 million patients in a large medical group practice. For each identified decision point, an encounter during which the patient's condition was not controlled, we compared the actual outcome of the treatment decision administered to that of the best-achieved outcome for similar patients in similar clinical situations. RESULTS: For the majority of decision points (66.8%, 59.0%, and 83.5% for HTN, T2DM, and HL, respectively), there were alternative treatment options administered to patients in the precision cohort that resulted in a significantly increased proportion of patients under control than the treatment option chosen for the index patient. The expected percentage of patients whose condition would have been controlled if the best-practice treatment option had been chosen would have been better than the actual percentage by: 36% (65.1% vs 48.0%, HTN), 68% (37.7% vs 22.5%, T2DM), and 138% (75.3% vs 31.7%, HL). CONCLUSION: Clinical guidelines are primarily based on the results of randomized controlled trials, which apply to a homogeneous subject population. Providing the effectiveness of various treatment options used in a precision cohort of patients similar to the index patient can provide complementary information to tailor guideline recommendations for individual patients and potentially improve outcomes.

Harnessing Full Value from the DoD Serum Repository and the Defense Medical Surveillance System.

The Army manages the Department of Defense Serum Repository (DoDSR) of over 43 million serum samples and the associated Defense Medical Surveillance System (DMSS) database that links individual service member characteristics to these biological samples. The main mission and use of these resources has been for military health surveillance. The Army turned to RAND Arroyo Center to systematically examine current requirements and capabilities of the DoDSR and DMSS, identify gaps, and suggest strategies to improve their ability to meet current and potential future military health needs, including surveillance, outbreak investigation, research, and clinical support, particularly as these relate to influenza and other infectious disease threats. The research drew information from written documents and interviews with military and civilian experts. The study identified a number of opportunities to improve the management, content, and use of the serum repository and associated database. There were six main recommendations: (1) clarify and communicate the missions of the DoDSR and DMSS both within and beyond the Department of Defense; (2) empower, structure, and resource the organizational oversight of DoDSR and DMSS so that they can fulfill the full range of their missions; (3) create an integrative data plan for comprehensive health surveillance; (4) enhance the utility of specimens; (5) plan for the next repository facility; and (6) raise awareness of and expand access to DoDSR and DMSS.

Will routine annual influenza prevention and control systems serve the United States well in a pandemic?

OBJECTIVE: To assess the extent to which the systems in place for prevention and control of routine annual influenza could provide the information and experience needed to manage a pandemic. METHODS: The authors conducted a qualitative assessment based on key informant interviews and the review of relevant documents. RESULTS: Although there are a number of systems in place that would likely serve the United States well in a pandemic, much of the information and experience needed to manage a pandemic optimally is not available. CONCLUSIONS: Systems in place for routine annual influenza prevention and control are necessary but not sufficient for managing a pandemic, nor are they used to their full potential for pandemic preparedness. Pandemic preparedness can be strengthened by building more explicitly upon routine influenza activities and the public health system's response to the unique challenges that arise each influenza season (eg, vaccine supply issues, higher than normal rates of influenza-related deaths).

Strategies to improve global influenza surveillance: a decision tool for policymakers.

BACKGROUND: Global pandemic influenza preparedness relies heavily on public health surveillance, but it is unclear that current surveillance fully meets pandemic preparedness needs. METHODS: We first developed a conceptual framework to help systematically identify strategies to improve the detection of an early case or cluster of novel human influenza disease during the pre-pandemic period. We then developed a process model (flow diagram) depicting nine major pathways through which a case in the community could be detected and confirmed, and mapped the improvement strategies onto this model. Finally, we developed an interactive decision tool by building quantitative measures of probability and time into each step of the process model and programming it to calculate the net probability and time required for case detection through each detection pathway. Input values for each step can be varied by users to assess the effects of different improvement strategies, alone or in combination. We illustrate application of the tool using hypothetical input data reflecting baseline and 12-month follow-up scenarios, following concurrent implementation of multiple improvement strategies. RESULTS: We compared outputs from the tool across detection pathways and across time, at baseline and 12-month follow up. The process model and outputs from the tool suggest that traditional efforts to build epidemiology and laboratory capacity are efficient strategies, as are more focused strategies within these, such as targeted laboratory testing; expedited specimen transport; use of technologies to streamline data flow; and improved reporting compliance. Other promising strategies stem from community detection - better harnessing of electronic data mining and establishment of community-based monitoring. CONCLUSION: Our practical tool allows policymakers to use their own realistic baseline values and program projections to assess the relative impact of different interventions to improve the probability and timeliness of detecting early human cases or clusters caused by a novel influenza virus, a possible harbinger of a new pandemic. Policymakers can use results to target investments to improve their surveillance infrastructure. Multi-national planners can also use the tool to help guide directions in surveillance system improvements more globally. Finally, our systematic approach can also be tailored to help improve surveillance for other diseases.

Methadone maintenance in prison: evaluation of a pilot program in Puerto Rico.

OBJECTIVES: To describe and evaluate a pilot methadone maintenance program for heroin-dependent inmates of Las Malvinas men's prison in San Juan, Puerto Rico. METHODS: Data from self-report of inmates' drug use before and during incarceration, attitudes about drug treatment in general and methadone maintenance in particular, and expectations about behaviors upon release from prison and from testing inmates' urine were analyzed comparing program patients (n=20) and inmates selected at random from the prison population (n=40). Qualitative data obtained by interviewing program staff, the correctional officers and superintendent, and commonwealth officials responsible for establishing and operating the program were analyzed to identify attitudes about methadone and program effectiveness. RESULTS: Heroin use among prisoners not in treatment was common; 58% reported any use while incarcerated and 38% reported use in past 30 days. All patients in the treatment program had used heroin in prison in the 30 days prior to enrolling in treatment. While in treatment, the percentage of patients not using heroin was reduced, according to both self-report and urine testing, to one in 18 (94% reduction) and one in 20 (95% reduction), respectively. Participation in treatment was associated with an increased acceptance of methadone maintenance. Prison personnel and commonwealth officials were supportive of the program. CONCLUSIONS: The program appears to be a success, and prison officials have begun an expansion from the current ceiling of 24 inmates to treat 300 or more inmates.