Emerging needs in behavioral health and the integrated care model

Medically vulnerable populations are constantly at risk of having poor health related outcomes, low satisfaction in the healthcare system and increased mortality. Studies have shown the increased prevalence rates of various medical comorbidities in patients with severe mental illness. These patients are obviously vulnerable because of their mental illness but they are also more likely to have severe cases of medical conditions commonly seen in the general population. Expenditures and utilization of resources is often inappropriate due to frequent visits for acute needs and low rates of preventative care and primary care appointments. My proposed model focuses on the implementation of the integrated care model which encourages collaboration between mental health professionals and primary care physicians through referral programs or integrated clinic settings. This model is initiated with education to both current clinicians as well as future clinicians through medical schools and residency programs. Once the education component has begun, the next steps are formal exploration, preparation, implementation and evaluation of the model in clinics. The aim is to improve health outcomes by increasing preventative care and using behavioral techniques to assist with adherence, increase satisfaction in the healthcare system and contain expenditures by utilizing primary care services instead of emergency services when appropriate

Keep it Simple? Predicting Primary Health Care Costs with Measures of Morbidity and Multimorbidity

In this paper we investigate the relationship between patients’ primary care costs (consultations, tests, drugs) and their age, gender, deprivation and alternative measures of their morbidity and multimorbidity. Such information is required in order to set capitation fees or budgets for general practices to cover their expenditure on providing primary care services. It is also useful to examine whether practices’ expenditure decisions vary equitably with patient characteristics. Electronic practice record keeping systems mean that there is very rich information on patient diagnoses. But the diagnostic information (with over 9000 possible diagnoses) is too detailed to be practicable for setting capitation fees or practice budgets. Some method of summarizing such information into more manageable measures of morbidity is required. We therefore compared the ability of eight measures of patient morbidity and multimorbidity to predict future primary care costs using data on 86,100 individuals in 174 English practices. The measures were derived from four morbidity descriptive systems (17 chronic diseases in the Quality and Outcomes Framework (QOF), 17 chronic diseases in the Charlson scheme, 114 Expanded Diagnosis Clusters (EDCs), and 68 Adjusted Clinical Groups (ACGs)). We found that, in general, for a given disease description system, counts of diseases and sets of disease dummy variables had similar explanatory power and that measures with more categories did better than those with fewer. The EDC measures performed best, followed by the QOF and ACG measures. The Charlson measures had the worst performance but still improved markedly on models containing only age, gender, deprivation and practice effects. Allowing for individual patient morbidity greatly reduced the association of age and cost. There was a pro-deprived bias in expenditure: after allowing for morbidity, patients in areas in the highest deprivation decile had costs which were 22% higher than those in the lowest deprivation decile. The predictive ability of the best performing morbidity and multimorbidity measures was very good for this type of individual level cross section data, with R2 ranging from 0.31 to 0.46. The statistical method of estimating the relationship between patient characteristics and costs was less important than the type of morbidity measure. Rankings of the morbidity and multimorbidity measures were broadly similar for generalised linear models with log link and Poisson errors and for OLS estimation. It would be currently feasible to combine the results from our study with the data on the number of patients with each QOF disease, which is available on all practices in England, to calculate budgets for general practices to cover their primary care costs.multimorbidity; primary care; utilisation; costs; deprivation; budgets

Clinical Data Reuse or Secondary Use: Current Status and Potential Future Progress

Objective: To perform a review of recent research in clinical data reuse or secondary use, and envision future advances in this field. Methods: The review is based on a large literature search in MEDLINE (through PubMed), conference proceedings, and the ACM Digital Library, focusing only on research published between 2005 and early 2016. Each selected publication was reviewed by the authors, and a structured analysis and summarization of its content was developed. Results: The initial search produced 359 publications, reduced after a manual examination of abstracts and full publications. The following aspects of clinical data reuse are discussed: motivations and challenges, privacy and ethical concerns, data integration and interoperability, data models and terminologies, unstructured data reuse, structured data mining, clinical practice and research integration, and examples of clinical data reuse (quality measurement and learning healthcare systems). Conclusion: Reuse of clinical data is a fast-growing field recognized as essential to realize the potentials for high quality healthcare, improved healthcare management, reduced healthcare costs, population health management, and effective clinical research

Association Between Medication Adherence and the Outcomes of Heart Failure

Background Previous studies of heart failure patients have demonstrated an association between cardiovascular medication adherence and hospitalizations or a composite end point of hospitalization and death. Few studies have assessed the impact of treatment adherence within large general medical populations that distinguish the health outcomes of emergency department visits, hospitalization, and death. Objective To determine the association of incremental cardiovascular medication adherence on emergency department visits, hospitalization, and death in adult heart failure patients in Indiana. Design Retrospective cohort study conducted using electronic health record data from the statewide Indiana Network for Patient Care (INPC) between 2004 and 2009. Methods Patients were at least 18 years of age with a diagnosis of heart failure and prescribed at least one cardiovascular medication for heart failure. Adherence was measured as the proportion of days covered (PDC) using pharmacy transaction data. Clinical end points included emergency department visits, hospital admissions, length of hospital stay, and mortality. Generalized linear models were used to determine the effect of a 10% increase in PDC on clinical end points adjusting for age, sex, race, Charlson comorbidity index, and medications. Results Electronic health records were available for 55,312 patients (mean age ± standard deviation [SD] 68 ± 16 years; 54% women; 65% white). Mean PDC for all heart failure medications was 63% ± 23%. For every 10% increase in PDC, emergency department visits decreased 11% (rate ratio [RR] 0.89; 95% confidence interval [CI] 0.89‐0.89), hospital admissions decreased 6% (RR 0.94; 95% CI 0.94‐0.94), total length of hospital stay decreased 1% (RR 0.99; 95% CI 0.99‐1.00), and all‐cause mortality decreased 9% (odds ratio 0.91; 95% CI 0.90‐0.92). Conclusion Incremental medication adherence was associated with reductions in emergency department visits, hospital admissions, length of hospital stay, and all‐cause mortality