Health Information Technology: Dos and Don'ts for Today and Tomorrow.

The challenges include not overburdening physicians and fitting into an increasingly complex, multilayered informatics ecosystem. Innovative health care entities that neglect health care's reliance on evidence-based medicine and go to market without the benefit of any peer review do so at their own peril.

A predictive model of hospitalization risk among disabled medicaid enrollees.

OBJECTIVES: To identify Medicaid patients, based on 1 year of administrative data, who were at high risk of admission to a hospital in the next year, and who were most likely to benefit from outreach and targeted interventions. STUDY DESIGN: Observational cohort study for predictive modeling. METHODS: Claims, enrollment, and eligibility data for 2007 from a state Medicaid program were used to provide the independent variables for a logistic regression model to predict inpatient stays in 2008 for fully covered, continuously enrolled, disabled members. The model was developed using a 50% random sample from the state and was validated against the other 50%. Further validation was carried out by applying the parameters from the model to data from a second state's disabled Medicaid population. RESULTS: The strongest predictors in the model developed from the first 50% sample were over age 65 years, inpatient stay(s) in 2007, and higher Charlson Comorbidity Index scores. The areas under the receiver operating characteristic curve for the model based on the 50% state sample and its application to the 2 other samples ranged from 0.79 to 0.81. Models developed independently for all 3 samples were as high as 0.86. The results show a consistent trend of more accurate prediction of hospitalization with increasing risk score. CONCLUSIONS: This is a fairly robust method for targeting Medicaid members with a high probability of future avoidable hospitalizations for possible case management or other interventions. Comparison with a second state's Medicaid program provides additional evidence for the usefulness of the model.

Integrating scientific and real-world evidence within and beyond the drug development process.

Newly developed healthcare treatments face a complex environment with many stakeholders who can accelerate or decelerate adoption, most notably the healthcare system payers. Understanding and integrating their needs earlier in clinical development will ensure a smoother transition from bench to bedside. This paper describes a new approach to shaping a more effective complementary process of 'value' evidence generation both in and outside the clinical drug development process. We propose that biopharmaceutical companies consider bringing new solutions to market by marshaling cross-functional approaches to what we term an evidence-definition phase, evidence-generation phase and evidence-translation phase to drug and technology research and development. The organization of ongoing discovery, evaluation and translation with a 'real- world' perspective should provide a more streamlined approach to ensure both regulatory and eventual marketplace success.

Predictive models for diabetes patients in Medicaid.

Predictive modeling can be used to identify persons who are at increased risk for adverse health outcomes. We used demographic, medical, and pharmacy claims data to create a gender-specific model for fee-for-service Medicaid based on 2 states' data that can assist with the identification of persons with an elevated future risk of hospitalization, elevated claims expense, or death. Depending on age and the outcome of interest, the area under the receiver operating characteristic curve for this predictive modeling tool across 2 states' diabetes populations ranged from 0.608 to 0.834. We conclude that this analysis yielded a level of accuracy comparable to other predictive models that can be used to target patient enrollment in population-based care management.

The patient-centered medical home for chronic illness: is it ready for prime time?

Robert Berenson and colleagues caution that the patient-centered medical home (PCMH) faces many challenges. Its successful adoption will depend on its being precisely defined and demonstration that it is cost saving and scalable across varied clinical settings. Until these issues are addressed in current and upcoming pilot programs, caution about the PCMH's role in the care of people with chronic illnesses is warranted.

Heart disease and managed care.

Although the use of beta blockers has measurably improved the state of cardiac care in the United States, more work remains to be done. Other meritorious interventions for cardiovascular conditions that deserve attention by MCO's. Given the continuing clinical and and cost burden of heart disease, MCO's can use the lessons learned from the success of beta blockers in other important areas of cardiovascular care, including longer-term beta-blocker therapy, treatment of lipid disorders, and use of aspirin and thienopyridine derivatives.

Back to the future: the managed care approach to rheumatoid arthritis.

Biologic disease-modifying antirheumatic drugs have been proven effective in the treatment of rheumatoid arthritis, but their efficacy literally comes at quite a high price to health plans. Therefore, MCOs are examining various strategies to provide coverage for these agents in a more cost-effective manner.

Reduced health care costs associated with disease management for chronic heart failure: a study using three methods to examine the financial impact of a heart failure disease management program among medicare advantage enrollees.

BACKGROUND: Disease management (DM) for chronic heart failure (CHF) has been examined in several randomized clinical trials. Most have shown improvements in quality and cost reductions. However, there is only limited information about the financial impact of CHF DM outside of research settings. METHODS AND RESULTS: Three commonly employed observational study methods (pre-post comparison, comparison of intervention patients to a convenience cohort and comparison of intervention patients to a propensity matched cohort) were used to assess the financial impact of DM for CHF for Medicare Advantage enrollees over different 1-year periods between 2001 and 2004. DM was associated with savings ranging from $318 to $708 per patient per month. Most savings appeared to be associated with reduced expense for hospitalizations and exceeded the cost of providing DM. CONCLUSION: CHF DM savings repeatedly demonstrated in randomized clinical trials are achievable in commercial insurance settings and exceed the cost of the intervention. Given the widespread interest in addressing the rising cost of health care, CHF DM remains an important consideration for Medicare beneficiaries.

It Ain't Necessarily So: The Electronic Health Record And The Unlikely Prospect Of Reducing Health Care Costs.

Electronic health record (EHR) advocates argue that EHRs lead to reduced errors and reduced costs. Many reports suggest otherwise. The EHR often leads to higher billings and declines in provider productivity with no change in provider-to-patient ratios. Error reduction is inconsistent and has yet to be linked to savings or malpractice premiums. As interest in patient-centeredness, shared decision making, teaming, group visits, open access, and accountability grows, the EHR is better viewed as an insufficient yet necessary ingredient. Absent other fundamental interventions that alter medical practice, it is unlikely that the U.S. health care bill will decline as a result of the EHR alone.

Obesity disease management opportunities and barriers.

Disease management, a system of coordinated health care interventions and communications for chronically ill populations, relies on patient education and case management to engage individuals in the management of their condition. Disease management also aims to enhance the quality of interactions between doctors and patients and advance evidence-based medicine. Because these programs' interventions frequently include helping individuals who suffer comorbidities associated with obesity to reduce their BMI, adaptation of disease management to populations with obesity seems a viable option. A major barrier for implementing disease management for obesity, however, is the lack of proven return on investment, which limits health plan and disease management organization interest. Purchaser demand may overcome this reluctance. Further research is needed to objectively test whether disease management interventions would be clinically effective for obese populations, produce positive financial outcomes for insurers, and enhance workplace productivity.

Disease management and the Medicare Modernization Act: "It's the insurance, stupid".

While definitions of "disease management" (DM) emphasize quality of care for populations with chronic illness, proponents argue it reduces healthcare costs. Buyers may find disease management organizations' (DMOs') use of clinical guidelines, physician collaboration, and promotion of patient self-management intuitively sound, but it is performance guarantees, combined with retrospective effectiveness cost studies, that have driven DMOs' penetration of the commercial insurance market with revenues that exceed $500 million per year. The success of DMOs contributed to the creation of the Chronic Care Improvement Program (CCIP), which is designed to prospectively test the impact of DM on both the quality and cost of care for fee-for-service Medicare beneficiaries with chronic illness. This may lead to an expansion of DM in Medicare, and even greater opportunities for DMOs beyond the $10 billion in 10- year projected growth. For community-based physicians caring for patients with chronic illness, the sharpened focus on chronic care and the growth of DMOs creates some potential advantages. These include more time to treat more patients with acute illness, lower practice costs, opportunities to collaborate over quality, and a greater ability to achieve quality targets set by pay-for-performance arrangements.