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How the health care data supply chain can help Medicaid


Medicaid programs can become more data-driven in their decision-making to drive better performance and more equity in value-based agreements.

Big changes are coming to Medicaid if the Biden administration has its way. In the future, the safety-net program is expected to increasingly emphasize health equity and value-based principles as it evolves to prioritize care coordination, measurable outcomes and provider accountability.

To achieve its goal of high-quality, affordable, person-centered care, the administration plans to transform Medicaid by centering the concept of equity on future payment models. “From here on, the Innovation Center will embed equity in every aspect of its models by seeking to include more providers serving low- and modest-income, racially diverse, and/or rural populations,” top CMS officials wrote in a published article in the journal Health Affairs.

Essential to this effort will be engaging more Medicaid providers in value-based contracts so their performance can be measured and Medicaid patients can be steered to the highest-quality, lowest-cost providers. That’s where the concept of the healthcare data supply chain enters the picture.

Reliable, accurate, high-quality data is critical to the success of any value-based care program, because it provides the foundation for measuring changes in care quality, cost-effectiveness, utilization, risk-adjustment, any other factors that form the basis of value-based agreements between payers and providers.

The health care data supply chain is a conceptual framework for describing the material, information, and services involved in producing and delivering a product or service to an end user. In the realm of the healthcare data supply chain, healthcare organizations’ own systems represent the supplier, the data itself is the raw material and the end users are patients, clinicians and other stakeholders.

By utilizing this concept, Medicaid programs can become more data-driven in their decision-making to drive better performance and more equity in value-based agreements.

The problem with health care’s approach to data
Since the advent of hospital information systems and electronic health records in the 1980s, health care has embraced a do-it-yourself mentality when it comes to data. This DIY approach typically starts with a massive effort to aggregate as much data as possible followed by an equally massive effort to find the expertise required to leverage that data. The result: years of expensive, slow, and cumbersome work whose value is imperceptible, at best.

One of the biggest problems with health care data is that overall time to value is very long. IT departments build an interface, test the interface and that interface gets deposited into a data warehouse. Then they start building a report and, finally, test that report.

Add to that the advancement of big data tech, where mature organizations have invested in an analytics function that spends significant time developing a complex artificial-intelligence-based tool or predictive algorithm, but don’t invest behind user experience features to actually take the output of the algorithm and put it into practice in a way that impacts anyone's life by improving patient care, operations or finances.

The problem lies in health care’s tremendous disconnect between the data supply chain and actual care delivery, represented by the difference between the data we have and the insights required for action. It’s almost like having the raw materials to make a car when the production side is tooled up to make computers.

Too often, the data that we need to transform care is simply not readily available and/or delivered in a way that is actionable and consumable by front-line caregivers in the ways that they need. This inability to package healthcare data for easy consumption is a classic supply chain problem that manifests itself in four primary ways:

Poor response times: Operating models, organizational layouts and processes aligned by functions create silos. Decision-making becomes linear. Delays arise as information is exchanged among silos, which causes the information to be out-of-date and lengthens supply chain response time.

Lack of visibility: Current technology and processes limit the ability to have end-to-end supply chain visibility at order, product or shipment levels. This results in inaccurate supply chain plans, higher fulfillment costs and an inability to sense problems as they occur and, ultimately, resolve them quickly.

Inefficient fulfillment model: Most companies’ fulfillment models seem to be static and struggling to manage the cost and complexity of the new omnichannel world. Traditional distribution and transportation models have been created to support high volumes and fewer SKUs. These models can prove to be inflexible and inefficient when extended to individual customized orders, wider product assortment and multiple last-mile delivery options of the omnichannel world.

Inflexible technology: Legacy technology platforms created around supply chain functions or silos are frequently time-consuming, costly to deploy and inflexible to extend to solve evolving business needs.

The health care data supply chain’s 3 key objectives
For a moment, take a step back from health care to consider that a modern, consumer-driven supply chain requires rapid acquisition and nimble adaption to evolving needs. For example, Amazon and Netflix have built a vast infrastructure that allows for instant gratification at a level of scale and reliability that is unmatched by competitors. This advantage is powered by a continuously running, curated, integrated, and growing supply chain that feels like a service working with and for their customers.

There is no reason that health care data shouldn’t show up for end users like a package, movie, book, or any other consumer good. The health care data supply chain must connect user demand to supply in a responsible way while accomplishing three key objectives:

  • Transforming real-time insights into strategic opportunities
  • Powering point-of-care decisions
  • Identifying the impact of operational and financial decisions

COVID-19 proved the urgency of data liquidity, in that organizations had to figure out how to proactively use their data to anticipate needs across a diverse set of stakeholders. The health care data supply chain is the first step in creating data liquidity. For Medicaid’s future of value-based care, data-guided decision-making, and emphasis on health equity, embracing the health care data supply chain concept may be the key to transformation.

Michael Meucci is Chief Operating Officer at Arcadia, a leading population health management and health intelligence platform. Arcadia transforms data from disparate sources into targeted insights, putting them in the decision-making workflow to improve lives and outcomes.

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