The transition to value-based care is something that many healthcare organizations are faced with today. Many payers...
are encouraging this new care model in hopes that it will help physicians focus on patient outcomes and population health, as well as curb rising healthcare costs. When it comes to achieving value-based care, several key elements must be adopted to ensure its success, and analytics plays a key role in that. However, there are a few obstacles healthcare providers may face when looking to adopt technologies to support value-based care initiatives.
Today, most healthcare providers have adopted some form of electronic charts in their organizations. These digital charts consist of data related to the patient's visit, scheduling, billing, labs and medication. Unfortunately, most EHRs are limited in what they are able to process and analyze when it comes to patient's data. Most have finite reporting capabilities that only provide basic information in regard to high-risk patients. This limitation is caused by the lack of advanced analytics capabilities, as well as the lack of comprehensive patient data that can provide enough information to be analyzed. As a result, physicians have limited or no capabilities around predictive care or high-risk patient detection.
Successful value-based care initiatives that prioritize improved outcomes use analytics to support preventive care and assist with the detection and management of high-risk patients. By analyzing patient data available to the healthcare organization, providers can gain insights that determine vulnerable patients based on different data elements that are collected and evaluated from EHRs, claims data, health information exchanges, wearables and other sources.
The use of analytics for value-based care initiatives can be seen in a number of different contexts ranging from clinical, operational or financial. Examples of analytics uses for value-based care include:
- interactive dashboards for clinical support teams that include data for at-risk patients and filtering capabilities;
- executive dashboards with operational and clinical key performance indicators;
- dashboards to summarize the effectiveness of proactive care and response to different treatment programs;
- traditional reports with details on patient lists and other relevant details;
- reports to track financial and quality metrics for the population and patients; and
- the use of AI algorithms and machine learning to detect patterns or determine patients based on specific pattern for certain conditions.
Unlike EHRs, analytics tools used for value-based care initiatives are not meant to collect or capture data. They are designed to provide feedback to end users based on existing data that has been analyzed and processed. They are also meant to be used continuously to gain insights and feedback on treatment programs and care being delivered to the community.
Despite the relevance and value analytics add to value-based care initiatives, healthcare organizations still face several obstacles when it comes to rolling these tools out. Some may be technical, while others may relate to data access, end-user adoption, security or a lack of understanding of the data itself. Regardless of the obstacles that lay in front of physicians, value-based care is here to stay. Payers are making it very clear that this is the direction they are moving in, and that physicians must adjust to this new care model to reduce healthcare costs and improve patient care.