THE OUTLOOK FOR HEALTH CARE IN 2023:

What you need to know about the forces reshaping our industry.

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Health care data blog series: Opportunity in health data is finally being unlocked. Here's how.

By Rob RyanJohn League

May 2, 2022

Just 10 to 15 years ago, most health care data was locked away in manila folders at physician offices and hospital records rooms. Paper-based record-keeping simultaneously prevented organizations from easily aggregating and analyzing clinical data to draw insights at scale. The result was a missed opportunity to make business and care models more smart, efficient, and equitable.

Today, health care organizations recognize the value of patient data beyond supporting clinical care and billing. Organizations are now focusing on opportunities to scalably draw insight from clinical data to support any number of clinical and commercial goals.

Health care needs a digital transformation. Is it ready?

According to Grand View Research, the health care analytics market reached $24B in revenue in 2020. Many of these investments have been very high profile, including Roche's acquisition of Flatiron in 2018 to the launch of Truveta in 2021.

It's easy to miss the forest for the trees in a market as buzzy and complex as the health care data market. In this blog, we'll deconstruct the value proposition health care organizations see in patient data and we'll provide an overview of the exploding vendor data market that supports those goals.

What are health care organizations trying to do with their data?

Use cases for health care data are many, but generally fall into three categories:

  1. Improve care protocols and treatment planning. Provider organizations and clinical societies seek aggregated clinical and claims data to determine the relative effectiveness of different treatment protocols and care pathways across entire populations. Health plans and risk-based providers similarly use big data to improve their patient risk stratification and provider engagement, enabling them to align their level of patient intervention with patients' relative clinical need. The central goal is to use data to understand how to allocate care delivery resources in such a way that maximizes quality and minimizes cost.
  2. Support business development and internal operations. Market-level claims datasets support strategic planning at hospital systems by demystifying the market's competitive landscape and informing opportunities for future growth. For life sciences companies, national claims datasets can inform site selection for clinical trials by identifying markets with high prevalence for relevant diseases and conditions. They can also support go-to-market strategy by uncovering the top referrers for a given drug. The central goal is to use data to target health care investment toward high-leverage opportunities.
  3. Power novel clinical and digital health innovations. Digital health platforms across modalities—from telehealth to remote patient monitoring to chronic care management—continuously collect and analyze data that helps drive process improvement and improved user experience. The data collected from these platforms can also be used to inform clinical decision making—for example, a physician monitoring a high-risk patient's blood pressure from a wearable device to track whether and when intervention may be necessary. The central goal is to use data to fundamentally reshape our approach to care delivery.

Executives across the industry increasingly recognize that patient data can support organizational goals beyond those which the datasets were initially created to serve. But challenges ranging from limited data access to poor interoperability to low actionability have led health care organizations to turn to specialized data vendors to help them make their data more usable.

What does the data vendor market look like?

Three types of healthcare analytics and data vendors exist to support health care organizations as they pursue these data-centered goals:

  1. Vendors that broker data directly (e.g., Datavant). These data vendors ingest health care providers' identifiable data and provide necessary data cleaning services—for example, deidentifying the data or tokenizing it so it can be connected to other datasets.
  2. Vendors that generate data through a product or service (e.g., Epic). Other data vendors offer a product or service to health care organizations that generate data in the process. In turn, these vendors often commercialize the data their products generate by selling the data to other organizations in a de-identified format. For example, Epic, an EHR vendor, offers a large database of de-identified patient records.
  3. Vendors that aggregate and package data into useful products (e.g., Komodo Health). Finally, some vendors acquire data and commercialize it by using the data to power tools that support their end users' goals. Organizations like Komodo Health, for example, offer dashboards to help inform site selection for clinical trials and understand the leading referring providers in a given market.

Fulfilling health care's data ambition

The industry has clear goals for its data and a market of vendors to support them. Nonetheless, barriers to success are ample. In our next two blogs in this series on health care data, we will outline the challenges to making better use of data; and we will propose potential solutions to these challenges.

Health care needs a digital transformation. Is it ready?

imageHealth care is behind nearly all other industries on digital transformation. Covid-19 created an opening for change—purchaser demand for digital care skyrocketed and the unsustainability of the industry’s digital-averse business models were exposed. Venture investment dollars have flowed freely ever since.

In other words, the ambition is large, and the stakes are high. Meanwhile, the barriers are many. Making health care truly digitally enabled will require changing the incentives that underlie industry dynamics altogether.

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