Health Plans Digital Transformation – Leveraging the Latest in Data Management

data management

How does Data Management impact healthcare?

Traditionally, healthcare organizations have been industry laggards in adopting the latest developments in digital technologies. Digital technologies, such as Artificial Intelligence (AI) and Big Data analytics, will drive the change in the quality of care delivery in the future. Quality measurements such as HEDIS and STAR have been around a while, (1; 2) and investments in multiple technologies, unused tools, and integration complexities have put a severe strain on Health Plan companies to achieve true benefits such as improved patient care, reduced operational burdens, and optimal use of resources.

In an increasingly complex Value-Based Care (VBC) payment models, Health Plan companies need to relook at their data and integration strategies in order to achieve higher efficiencies in their business. Data management is vital to this. These quality measures impact a health plan’s profitability, as well as provider and pharmacy organizations’ value-based care incentives. Low performance of quality measures downgrades overall plan ratings and may impact the financial performance of the payer organization (3). It is imperative for payers to improve these scores as millions of dollars are at stake based on a single metric going up or down even a half-point. Leveraging the right technology with holistic data philosophy will be the key to improving these quality metrics. Some of the key digital technology levers such as big data, cloud, Artificial Intelligence (AI)/Machine Learning (ML), and modern data warehouse and analytics platforms help to improve health data management; subsequently improve reporting, and ultimately standard of care (4).

Database technologies and Business Intelligence (BI)/Analytics have been in the industry for decades. In a digital world, data is the new oil – and this carries even greater importance for Healthcare organizations. The Health Insurance Portability and Accountability Act (HIPAA) and other regulations add more complexities in the way data is managed in the Healthcare industry. New digital technologies focus on key tenants such as data security, privacy, data silo issues, and budget constraints, and form the basis for innovative solutions to address industry problems. Also, as Artificial Intelligence and Machine Learning technologies mature and adoption continues to increase across industries, especially in healthcare, we predict their increase for Predictive Analytics with Health plan organizations.

Key Aspects that help Health Plan Organizations to Improve Quality Measurements:

  1. Data Ingestion and Data Silos: As the data volume increases in quantity and source variation on the enterprise platform, having the right data ingestion mechanism is critical and paramount. Health data is gathered from multiple sources/applications, including electronic health records (EHRs), diagnostic, imaging, provider repository, enrollment records, claims data, billing, and medical devices, to name a few. In almost all enterprises, data silos exist through these applications (5), which can produce structured, semi-structured, or unstructured data at different velocity and in different volumes. With diverse data formats, structure, and context, it is enormously challenging to gather, process, and harness key information into useful, actionable assets. Recent developments in Big Data integration enable enterprises to more efficiently manage data silos, which then can generate tremendous value in terms of improved data mapping and data accuracy.
data management and health plan digital transformation
  • Data Integration and Data Lakes: Integrating varied data silos for effective data sharing while protecting the privacy and security of patient data is also a critical function for enterprises. Advancements in Big Data technologies and infrastructure, including distributed processing and messaging, make it much easier to integrate a variety of data sources at high scale.  (6). Spark and Hadoop can be better to integrate a variety of data sources (7). Kafka and other distribution messaging technology make real-time data integration possible (6). Data Lakes are an excellent choice for data consolidation and for integrating data silos. Secondly, having a core operational platform that creates a single point of truth for all stakeholders is key, as is having the capability to share data with external collaborators when needed. Integrating data across multiple, disparate systems will improve operational and financial viability of VBC programs for payer organizations.
  • Big Data Warehouse: Health plan companies have tried and used Hadoop and other Big Data technologies for a while with less success due to complexities involved in engineering and operationalizing these tools to drive top business benefits. Innovative technologies deliver an integrated approach to capture and analyze data from the data lakes in a streamlined and structured manner, and this is a critical element in reducing these complexities with less manual intervention by the enterprise.
  • Analytics & Reporting: Only 0.05% of all the healthcare data available is actually being analyzed for operational decision-making. Accurate, timely, and seamless reporting capabilities across the care continuum improves transparency and trust between payers and providers. Clinical insights from Big Data analysis allow providers to prescribe treatments and make clinical decisions with greater accuracy resulting in lower costs and enhanced patient care. Insights on patient cohorts that are at greatest risk for illness can lead to a proactive approach to prevention (5). These insights can be used to educate patients to take responsibility for their own wellness. Bringing financial and clinical data together highlights the efficiencies and overall effectiveness of treatment plans. Thus, utilizing Big Data analysis tools and dynamic reporting translates to better patient care, shorter hospital stays, and fewer re-admissions – translating to reduced cost to the enterprise in the long run.

Summary/Conclusion

Improving Quality metrics using key digital technologies like Big Data, cloud, AI/ML are in high demand across the healthcare industry. The key benefits these deliver include:

  • Enhanced predictive analytics to more accurately identify high or low-efficiency providers.
  • Improved reporting to enable providers to foster incentives for increased efficiency.
  • Increased engagement and satisfaction through deployment of the correct data strategies.
  • Quality metrics to achieve a better quality of care.

By leveraging appropriate big data technologies to create strong data management, we can move toward value-based healthcare while reducing costs. With the treasure trove of information that Big Data analytics provides, payers and providers can now make better medical and financial decisions while still delivering an ever-increasing quality of patient care.

Data Management Article References

1. CMS. 2019 Part C and D Star Ratings Fact Sheet.  [Online] https://www.bettermedicarealliance.org/sites/default/files/2019_Star_Ratings_Fact_Sheet_2018_10_09.pdf.

2. HEDIS Measures and Technical Resources. [Online] https://www.ncqa.org/hedis/measures/.

3. The Quality of Outpatient Care Delivered to Adults in the United States, 2002 to 2013. Levine, David M., Linder, Jeffrey A. and Landon, Bruce E. 12, 2016, JAMA Intern Med. , Vol. 176.

4. Healthcare quality management market, global forecast to 2022. s.l. : Markets and Markets, 2016.

5. Healthcare Big Data and the Promise of Value-Based Care. [Online] https://catalyst.nejm.org/doi/full/10.1056/CAT.18.0290.

6. Overcoming data silos through big data integration. Patel, Jayesh. 1, 2019, international Journal of Computer Science and Technology, Vol. 3.

7. Large Scale Distributed Data Science Using Apache Spark. Dai, J. G. Shanahan, and L., 2015. 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.

Summary:

Data Management for Health Plans

Traditionally, healthcare organizations have been industry laggards in adopting the latest developments in digital technologies. Digital technologies, such as Artificial Intelligence (AI) and Big Data analytics, will drive the change in the quality of care delivery in the future. In an increasingly complex Value-Based Care (VBC) payment models, Health Plan companies need to relook at their data and integration strategies in order to achieve higher efficiencies in their business. Database technologies and Business Intelligence (BI)/Analytics have been in the industry for decades. In a digital world, data is the new oil – and this carries even greater importance for Healthcare organizations. Key Aspects that help Health Plan Organizations to Improve Quality Measurements: 1. Data Ingestion and Data Silos. 2. Data Integration and Data Lakes. 3. Big Data Warehouse. 4. Analytics & Reporting. By leveraging appropriate big data technologies, we can move toward value-based healthcare while reducing costs. With the treasure trove of information that Big Data analytics provides, payers and providers can now make better medical and financial decisions while still delivering an ever-increasing quality of patient care.

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Giri Rajaiah

Giri Rajaiah

Mr. Rajaiah is the VP for Healthcare and Lifesciences at DISYS. He is responsible for fueling DISYS’ growth through innovative Healthcare domain-based solutions that address key industry issues through strategic partnerships with technology companies. Mr. Rajaiah has also led large Digital Transformation practice groups and brings more than 25 years of experience driving multifold growth across industry domains including Healthcare and Life Sciences, the US Federal Government & State Health and Human Services Departments. To learn more about DISYS, please visit www.disys.com.