By Dr. Vishal Anand, Healthcare BA and Prafull Mathur, Healthcare Business Analyst
So, one of the most important pillars of data-driven digital transformation and competitive positioning for Payviders is data management. With huge volumes of data coming from different sources in multiple formats and velocities, it becomes absolutely essential for payvider organizations to have the best data management strategies and solutions that would enable them to thrive and excel among peers.
In the previous blog of our payvider value chain blog series, we had discussed different aspects of data integration and interoperability. This blog would bring forth the issues of data curation and management and what importance does it hold for Payviders.
In this blog we would cover various aspects of data management like industry trends, key challenges and solution considerations for Payviders.
Industry Trends
1. Shift towards value-based care
Industry is moving towards value-based care payment models. Value-based care means increased financial risk to healthcare organizations. Data-driven insights surrounding every aspect of care delivery be it quality, cost, health outcomes, patient engagement etc. is playing a major role in helping this shift towards value-based care.
2. Clinical data integration imperative
Clinical data integration helps health plans in providing a unified view of data to better understand population-level health, quantify risk, improve workflow efficiencies, and manage regulatory compliance. It enables health plans including Payviders to acquire, access and share clinical data to improve patient outcomes, reduce cost and increase revenue.
3. Expanding scope of healthcare data
With the advent of Covid pandemic, healthcare needs of the population have increased significantly. There has also been increase in virtual care with real-time IoMT devices capturing, recording and transmitting data. Behavioral health is also making it imperative to access and analyze social determinants and social media data. This has led to unprecedented increase in volume, variety and velocity of data.
4. Increase in demand for real-time / streaming analytics
Real-time event notifications and point of care decision support has become the need of the hour for healthcare organizations to drive proactive interventions and manage risk.
5. Advancements in big data and cloud technologies
Adoption of cloud computing has increased to handle huge data sets, optimize costs, make information more secure and personalized. In the coming years, more payer and payvider organizations would be willing to invest in cloud-based solutions.
Key Challenges
The increasing variety, velocity and volume of healthcare-related data poses significant challenges in the data lifecycle across ingestion, curation, aggregation and overall governance.
1. Data Ingestion
Data coming in from different source systems (clinician notes, X-Ray, MRI, social media feeds, EHRs, etc.) and generating different type of data is the biggest challenge faced by the payvider organizations. What earlier was just a standard technology to acquire data now requires hybrid technologies and tools to extract data from structured and unstructured sources. Data ingestion poses another challenge as we have different type of healthcare data (streaming, complex, unstructured, images, etc.) which needs to be ingested with high velocity to uncover insights hidden in data, that can be used for business advantage as organizations today rely heavily on data for predicting trends, forecasting the market, planning for future requirements, understanding consumers, and business decisions.
2. Data Aggregation & Curation
Data aggregation has become a major challenge for Payviders. Not only does the data constitute core payer entities like claims, membership, etc., provider/clinical entities such as vitals, lab results, etc. but also newer data such as genomics, medical device data, patient-generated health data, etc. Curation of the data would include all aspects of data processing including conversion of raw data to machine-readable / usable form, mapping to operational models, quality & standardization checks, reconciling insights from structured/unstructured/complex data. It also extends into training / re-training of data science / AI-ML models. Due to the diversity of data sources and formats, there is an ever-increasing number of data sets, leading to disparity in the data. Harmonizing all this data on an ongoing basis is very challenging for Payviders. Also, the need for high performance (Volume, Variety, Velocity, Veracity & validity) only adds to the challenge.
3. Data Governance
Efficient Data governance is quintessential for data management practice to maintain data consistency, quality, integrity, and reliability . With the humongous increase in volume, variety and velocity of data and the health information exchange imperatives, it becomes a challenge for payvider organizations to govern the different data sources and formats and in turn make sure that the data quality and the information security does not get compromised. On top of that the changing regulations and compliance needs are making the challenges more acute. Data compliance is a challenge that caters to concerns from patients and other data providers with respect to the PHI data. Another challenge that comes with huge set of data files is data cataloging for different type of users (researchers, data scientists, etc.) with a need to organize & define data sets as per business taxonomy so that it becomes easy for users who are going to use it.
Solution Considerations
1. Single source of truth to drive holistic decision making:
Payvider organizations need to consolidate data acquisition, harmonization, and provisioning to create a single source of truth and build a holistic environment that serves as a single, secure point of access and provides unified view of data to downstream systems. For an organization to achieve SSOT, an integration strategy is needed, as well as an interface that will host and surface the organization’s data.
To judge the effectiveness & efficiency of the entire organization, the organizations need to have the ability to see all their performance results together, in one place to have a unified view covering all the metrices and KPIs including clinical, financial, SCM, operational and administrative, along with correlations therein.
SSOT provides an overarching view combining the important aspects of quality, cost, utilization, experience, physician/nursing staff satisfaction and financial performance that helps in driving business decision based on the insights generated from these reconciled & standardized data sets.
2. Modern enterprise data strategy to align with priorities & speed of business
Payviders need to adopt modern enterprise data strategy with next-gen data management capabilities for faster adaptability and time to market. The modern data enterprise strategy would enable organizations to be up to date when it comes to enhancement, augmenting and managing data quality, security, and governance processes. It would also enable architecture to support newer data formats, facilitate scalability for feature engineering and future demand. Some of the modern data strategy solutions include:
-
-
Enterprise Data Lake: Within lake house we need to create different layers or zones so that data can be worked upon. Data lake house concept comes handy over here. It combines the advantages of data lake and data warehouse and enables building a transactional layer on top of data lake. The advantages include elimination of simple ETL jobs, reduced data redundancy, ease of data governance, direct connection to BI tools and cost reduction.
-
Enterprise Data Hub: An enterprise data hub / operational data store (ODS) would help in providing organizations with a centralized and unified data source which can be used by users to apply analytics and data mining tools on top of it.
-
Analytics Hubs: It would enable to create subject-area / use-case specific marts for various analytics and research use cases leveraging data from different layers of the data architecture include data de-identification, synthetic data generation, etc. in a streamlined and controlled manner.
-
Enterprise API gateway: As interoperability takes the next step towards more API based data exchange through standards such as FHIR, this would enable secure, seamless yet controlled sharing of information within and outside the system.
-
Real-time data streaming: It helps the organization to quickly extract information, respond faster and drive proactive interventions from streaming and voluminous data sources such as patient-generated health data (PGHD) from Internet of Medical Things (IoMT), social media streams, virtual/video streams, etc.
3. Efficient and integrated DataOps to optimize data curation and data governance
It is imperative for Payviders to align data strategies with the shifting priorities and increasing speed of business by employing the right DataOps (data-in > information-out) tools and techniques for faster speed to value. Payviders should be looking at improving data quality and delivering better insights from their data without sacrificing speed and quality.
An efficient and integrated DataOps strategy would lead to effective data management involving ingestion, curation, aggregation, provisioning and governance. It would ensure that the organization’s data can be used in the most flexible, effective manner possible to achieve positive business outcomes.
In summary, data management is one of the most critical areas to truly drive Payvider value chain excellence. With the healthcare industry continuously facing multitude of business and technology changes - tightening regulations, shift from volume to value, rising consumerism, virtual/remote care, growing data complexities (volume, variety, velocity, veracity), technology advancements (big data, cloud, API, IoT, etc.), et al. it has become imperative for payviders to adopt modern data management strategies to successfully deliver on the promises of quality, cost, experience & growth.
-
