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Value-based Healthcare: The Paradigm Shift in Health Industry through Data and Analytics

Value-based healthcare is a model where healthcare providers are paid based on the outcome of a patient’s health while maintaining overall cost efficiency. In today’s scenario, most of the advanced countries like the USA, Japan, Canada, Germany have started aligning towards a value-based healthcare system because of its promised sustainability and flexibility. The importance of this is irrefutable in today's highly competitive healthcare sector, which offers alternative care options for the patients. With the strong penetration of the internet, information is readily available, and patients increasingly seek personalized healthcare. Moreover, value-based healthcare is rising in popularity and acceptance as modern healthcare informatics enables the collection and dissemination of outcome data with all stakeholders.

In a value-based care concept, an essential aspect of generating value is about identifying & reducing risk propensities of a patient’s health.  With this ready data at disposal, the predictive analytics technique has immense potential to be a game-changer for the Healthcare provider world. According to a recent survey from the Society of Actuaries (SOA), 60% of healthcare executives are using predictive analytics within their organization. Further, 20% of payers and providers are planning to start using predictive analytics within the next year. According to another recent survey by Black Book Research, 76% of hospitals and health system executives expect to dedicate at least 10% or more of their 2020 IT budgets to predictive analytics.

Predictive analytics includes a variety of essential steps like data warehousing, data filtration, and the statistical techniques which include

•    Data mining (to fetch valuable data from an abundant repository)

•    Predictive modeling (to draw insights and make sense out of it)

•    Machine learning (to replicate the model on its own and to evolve)

•    Analyzing current and historical data to make predictions

 

Some of the most prominent use cases that the industry is looking at for predictive analytics implementation includes:

1. Increase the accuracy rate of diagnoses: Doctors currently have very little time to analyze every single patient. If they get accurate data from the past and the possible predictions about the health of that patient, it will assist the doctor in giving an accurate treatment in a shorter time. It will increase patient satisfaction rate as well.

2.  Risk reduction: A risk score is created for individuals, based on the data collected from lab tests, health check-ups, patient-generated health records, and various other determinants of health. This risk score helps for the early detection of any disease, which reduces the risk of the illness escalating further.

3. Improve patient’s psychic Health: Predictive algorithms can also capture the psychometric data of a patient. That way, doctors can understand not only physical health but also the mental health of a patient and prescribe the medications accordingly. 

4. Reduce re-admission rate: Patient re-admission rate is one of the significant challenges for hospitals. Using Big Data analytics/Predictive analytics hospitals can reduce this rate for a certain time. Intel and Cloudera use predictive analytics to help a large number of hospitals reduce repeat patient cases.

5. Data Security: Patient’s data or research data based on thousands of diagnoses serves as valuable for an outside firm. In this case, predictive analytics plays a vital role in cybersecurity. For each transaction, it generates a risk score, based on which cyber-attacks can be prevented. Using the predictive tools, organizations can get early warnings if something changes in the data pattern, which can indicate a cyber-attack or an intruder activity.

There is a lot of data that is getting generated for the patients by the hospitals, health centers, and patients themselves. While some of this data that is generated is useful,  some might not apply in the context of a particular patient’s health regime. For ensuring the success of the implementation of predictive analytics, stakeholders involved must understand and identify the source of such data, analyze and utilize for prediction and early diagnosis of a particular health complication with the right combination. By taking the right decisions at the right time by ensuring the smooth adoption of predictive analytics across verticals of the healthcare sector, policymakers can enable and scale value-based care for the population.

Author: Priyanka Rakshit, Insurance & HealthCare team