Milliman MedInsight Perspective: Driving digital transformation in health plans

By Milliman MedInsight

5 June 2024

Digital transformation in healthcare is not just about adopting newer, faster technologies. It is about fundamentally changing how healthcare is coordinated and delivered. To advance quality, affordable care, health plans must be able to quickly make sense of the vast amount of data available today. This includes integrating data such as claims, clinical records, and member information with digital services across an organization. When it comes to enabling value-based care and outcomes, digital transformation is no longer an option for health plans, but a necessity. It holds the key to improving quality of care, streamlining operations, and staying competitive in the rapidly evolving healthcare landscape.

Recently, we had the opportunity to sit down with Marcos Dachary, Chief Market Strategist and General Manager of Payer Solutions at Milliman MedInsight (MedInsight). During this discussion, he shared valuable insights into how analytics can help health plans continue down the path of growth and innovation with strategic digital transformation.

Q. What is the impact of digital transformation on payers?

A. The healthcare industry is currently undergoing a significant digital transformation. Health plans recognize the need to modernize their business and architecture to tackle complex healthcare challenges. By prioritizing interoperability, quality, and value-based care, health plans can better align themselves with market demands and serve their members. It is important, however, to understand that there is no “one-size-fits-all approach” to digital transformation. A successful digital transformation requires a strategic approach, strong leadership, and a willingness to embrace change. Each organization must consider their current infrastructure, past technology investments, vision, and growth strategy before embarking on their digital transformation journey.

Managing enterprise data analytics is a complex and demanding task. It requires teams to critically evaluate their strengths and decide whether to develop in-house solutions or purchase external ones. This decision is vital to ensure that IT efforts align with the primary needs of the insurance business, rather than getting sidetracked by software development. Effective data-driven decision-making is crucial for propelling the business forward swiftly. However, financial limitations often restrict the resources available for exploring new revenue opportunities and enhancing efficiency. This makes proficiency in feature engineering essential to keep up with industry trends.

Moreover, the ongoing costs associated with maintaining systems can significantly impede innovation and the speed of organizational progress, acting like cement shoes that hold back rapid advancement. To overcome these challenges, companies should consider adopting strategies that complement their data models, such as integrating artificial intelligence, and looking into offshoring and outsourcing. These approaches can greatly reduce maintenance costs and foster quicker innovation within the organization.

The effectiveness of any analytics solution, however, relies heavily on the quality of the data. To foster an aligned, collaborative user community within a health plan, users must have trust and confidence in their data. When health plans break down the barriers created by different data languages, they can more effectively establish a common language across the enterprise. A scalable platform that delivers a single source of truth can help health plans with alignment and goal setting, and ultimately offer a more member-centric approach.

Q. What hurdles do health plans face with data and analytics?

A. Health plans that use fragmented data and systems tend to encounter significant challenges with gaining speed to insights. One step a health plan can take is to establish a data governance framework. This is particularly relevant as more organizations consider the implementation of artificial intelligence (AI) and generative artificial intelligence (Gen AI). A balanced data governance model is crucial for healthcare organizations to succeed with analytics. While overly strict guidelines can restrict data, decentralized models can lead to inefficiencies and redundancy. Achieving the right balance in the data governance model can support the generation of meaningful and actionable analytic insights.

At MedInsight, we provide guidance to health plans in developing a data governance model. Our main goal is to ensure that MedInsight’s strong data standards and requirements, including those for the MedInsight Data Confidence Model, are smoothly incorporated into the overall data governance model and processes. This approach helps health plans utilize data efficiently and responsibly, while also allowing the data governance model and policies to adapt to the organization’s evolving needs.

Q. What analytic capabilities are important for health plans to consider?

A. The importance of third-party benchmarks cannot be overstated in today’s environment. They are pivotal not just for accelerating change through digital transformation but also for ensuring that the insights health plans gain are both valuable and practical for their business operations. Additionally, as plans empower more team members within their organizations, the distinction between “certified” and “non-certified” measures grows increasingly important. This is because strong data governance is essential to maintain the integrity and usefulness of the data. By contextually validating these measures according to specific use cases, health plans can strategically deploy their data analytics resources to achieve maximum efficiency and effectiveness.

Q. What options are available to health plans striving to create a more connected ecosystem? How can MedInsight’s next-generation Payer Platform help them achieve their goals?

A. Health plans struggling to drive digital transformation may consider partnering with an analytics vendor that excels in data aggregation. MedInsight has extensive experience collaborating with numerous payer organizations and processes hundreds of data files monthly. In fact, we have developed a custom Extract, Transform & Load (ETL) process specific to each customer’s data set and can accept data feeds in any format. Moreover, our database incorporates an integrated data model that promotes data standardization and normalization that guarantees timely delivery and minimizes data corruption. We pride ourselves on quick turnaround times, measuring data aggregation and loads in days, not weeks or months.

Furthermore, our newly expanded Payer Platform provides true economies of scale at the enterprise level. We empower health plans to enhance their reach and performance through the seamless integration of cloud-based technology and our industry expertise. With the Payer Platform, health plan customers can tackle complex questions by sharing referential data, benchmarks, and other tools with care teams, support teams, and operations. Our innovative applications center on value-based care, provider analytics, employer group reporting, and artificial intelligence to further enhance a health plan’s analytics capabilities. Looking forward to the MedInsight roadmap, there’s a lot to be excited about, particularly the initial phases of developing the Data Confidence Model AI. This development is expected to be a game-changer in the industry.

Lastly, we must not forget that Electronic Health Records (EHRs) are a key component of digital transformation in healthcare. When appropriately integrated with claims data, they can streamline operations and improve efficiency. By partnering with MedInsight, health plans can minimize the risk of errors and access proactive insights to improve health outcomes and contractual performance. We currently collaborate with leading EHR vendors like Epic and Cerner to equip population health teams with timely and accurate information. This empowers health plan organizations to further enhance the quality of care and mitigate financial risks effectively.

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