Defining value—the foundation of outcomes-based risk-sharing agreements health affairs electricity laws in india


No matter our roles in the health care system, we are all striving for the same result: high-quality, high-value patient care. Yet, in a complex gastric sleeve scars, constantly evolving system with so many players, making value-based care a reality is challenging. New ways of thinking and operating are required to bring a value-based system to fruition. The “Learning Laboratory”

In 2017, Merck and Company and Optum began a collaboration called the “ Learning Laboratory” to help advance our common goals of improving patient health outcomes, expanding access to innovative therapies, and ensuring the best use of health care spending. The collaboration focused on two therapeutic areas: prodromal (or early-stage) Alzheimer’s disease, a chronic condition predominately managed in an outpatient setting, and clostridium difficile (C-diff) infection, an acute bacterial condition that can be treated in either inpatient or outpatient settings.

As part of the Learning Laboratory, Merck and Optum explored different value-based contracting models, also referred to as outcomes-based risk-sharing agreements (OBRSAs), that reward innovative treatments with a clear and measurable impact electricity labs high school on patient outcomes. Despite having broad support, OBRSAs remain at the periphery of our system; very few have been implemented with much success.

The first year of our work together was eye-opening. Even though we were both talking about OBRSAs, our perspectives were often very different. It was like looking at the same gaston y daniela coin but from opposite sides. At the heart of the difference in perspectives were the different ways we define value: Is the right question whether the technology or treatment is a good value for the money, or whether the payer can feasibly afford the technology or treatment?

Our technical approaches were also different. For example, Optum’s financial analyses done on behalf of payers tend to emphasize all the aspects of health care for which a health plan is financially responsible, such as medical and pharmacy spend across all conditions a patient may have. These financial analyses have three-year time horizons with frequent updates to reflect new patient cohorts and care practice. In contrast, Merck’s health economic framework looks at spending for a specific disease area, over longer time spans, and can include broader value assessments. These assessments can include factors such as caregiver costs or the ability of treatments to reduce other costs in the health system—hospitalization, for example.

These two approaches yielded similar results for C-diff infection gas in stomach but different results for prodromal Alzheimer’s disease, likely due to the acute nature of C-diff infection and the chronic nature of prodromal Alzheimer’s disease. The fact that the disease burden for prodromal Alzheimer’s disease is primarily absorbed by patients’ family members, not health plans as with C-diff infection, also contributed to the differences. Another electricity test physics factor driving the different results for prodromal Alzheimer’s disease was the question of which treatments should be considered as relevant comparators to a new therapy. For example, should an innovative disease-modifying therapy be compared against those that only treat the symptoms? On the other hand, the comparators to the new C-diff infection treatments are well established according to the current clinical guideline.

Another important learning was the need to develop methods of linking payments for treatment with patient outcomes. The inability to access the necessary data and adjudicate outcomes cause many to ask, “Is the juice worth the squeeze?” In other words, are the technical challenges of setting up and implementing a risk-sharing contract worth the benefits that it would bring?

As part of our commitment to finding practical solutions, we developed an innovative contract simulation model that links outcomes and payments. Instead of creating a single, one-off final model, we experimented with gas vs electric range different contract designs to determine if there are ways to achieve better care for less money while preserving innovation.

The contract simulation model contains four main modules: target population, disease model, clinical outcome assessment, and contract terms. The outputs of the model include health plan and biopharmaceutical perspectives in terms of total revenue and expense, rebates, and OBRSA implementation costs. Per-member-per-month cost is also reported as a key consideration from the health plan perspective. The simulation model allows users to compose 9gag wiki a contract scenario by choosing a particular target population, clinical outcome, and a set of contract terms. Users can then compare the outputs across multiple contract scenarios to identify the “win-win” scenario based on risk-benefit trade-off between the two contracting parties.

By evaluating radically different diseases and exploring gaps in understanding, we avoided pitfalls that would have knocked a traditional negotiation off track. We asked what kind of data are really needed to inform and facilitate negotiation. What kind of data infrastructure is required? How must our rules of engagement change to permit the kind of productive dialogue we need?

Given their simplicity and availability, we acknowledge that claims data should be the starting point for contract adjudication. However, this project also revealed that using claims data alone limits the ability to specify both the patient and outcomes of interest. For example, it is hard to confirm a C-diff infection case because diarrhea, a common C-diff infection symptom gaz 67 sprzedam, is a symptom common in other conditions.

With prodromal Alzheimer’s disease, claims data alone can enable an OBRSA based on time-to-Alzheimer’s dementia diagnosis as the clinical outcome. However, claims data alone cannot support an OBRSA based on patients’ cognitive or functional assessments, which are the common endpoints in clinical trials. In addition, endpoints used in regulatory gas density units approval and product labeling are often different from those that are available in routine claims data. Early Learnings And The Path Forward

Throughout our work together, we realized that there were a variety of gaps between our languages, methods, and value drivers. To close those gaps, we identified contextual and foundational themes to guide our engagement. One of those critical themes was uncertainty, which can be perceived as risk or opportunity depending on one’s perspective. We found that an early focus on and quantification of uncertainty enabled a productive and efficient engagement.

Our collaboration has led to an increased understanding that payers and biopharmaceutical companies can take into future discussions. We plan to publish many of the findings of our work in the months ahead so that others can learn from the range of contracting models we explored and from what worked and what did not. Ultimately, we hope that the gasbuddy touch Learning Laboratory will help facilitate the move to value-based care and improve patient access to needed medicines.