Technology Itself Doesn’t Solve Healthcare’s Biggest Tech Challenges

Healthcare Technology Consulting Firm

As technology becomes ever more ubiquitous, healthcare leaders are acknowledging the fact that standing up a new solution doesn’t solve anything on its own. Rather, health IT serves as an enabler — a tool that helps humans achieve. How humans interact with technology is far more important than the actual platform the business relies on.

Too often, healthcare organizations identify a technology need then seek out a specific solution. While they might have a clear idea of the challenges they’re looking to solve, they don’t always take a step back to truly understand how a certain solution will fit within the organization’s workflow or its existing technology ecosystem.

The digital health panorama

In 2020 as the pandemic kept Americans in their homes, telehealth became the novel innovation that drew the attention of delivery systems as well as investors. Utilization hit an all-time high in the spring as many patients took advantage of the convenience, including seniors enrolled in Medicare. About half of seniors who had a regular source of care and whose care provider offered telehealth engaged in a telephone or video visit.  

Telehealth has great promise, of course. But it’s only one component in the groundswell of the larger digital health panorama.

Leaders must think through their overarching technology approach before committing to any technology strategy or solution. Key members of the clinical team are often part of larger C-suite discussions, and technology leaders should come to the table as well.

Artificial intelligence in healthcare

The future of healthcare technology includes powerful data analytics that will drive clinical decisions and speed up administrative tasks. Take artificial intelligence (AI) for example. AI processing essentially simulates “smart” human thinking.

By distilling a huge cache of data points, AI algorithms uncover patterns and draw conclusions, even filling in the blanks based on continuing experience with the data in question. AI can churn terabytes of data to reduce standard errors and detect anomalies, eventually distilling data in such a way as to bring about new conclusions.

In other words, AI answers tough questions. The role of the human is to ask the right questions and then put AI’s answers to work.

In healthcare, we’ve only just begun to scratch the surface of AI and its possibilities. Several large companies recently invested in AI, undoubtedly viewing it as a profitable addition to the enterprise.

For example, Microsoft recently purchased Nuance for $16 billion to develop medical transcription capabilities with AI voice technology. In April, the Mayo Clinic launched two joint ventures to deliver AI clinical decision support, diagnostic insights, and care recommendations. Payers, providers, and app makers are all doubling down on AI’s potential.

Putting technology to work

New technologies seem to hit the streets every day, and decision-makers might find the process of adopting the best solutions for their business challenging at times. A better approach is to first develop a target operating model with the goal of maximizing the utility of the technology tools in use.

The target operating model should outline:

  • End-to-end processes
  • The people involved in each activity
  • What data is flowing in and where
  • What data is flowing out and where
  • Which tools facilitate each activity
  • Where multiple processes intersect
  • Where new processes must be introduced

Always look upstream in terms of inputs and downstream in terms of who will consume the resulting insights and for what purpose. Since the idea is to maximize the effectiveness of the technology — what you have now as well as what you might want to purchase — it’s a worthwhile exercise to revisit the operating model with an eye for the practical results.

Healthcare companies today have millions of dollars sunk into state-of-the-art analytics, but they don’t always understand the quality of the data they’re funneling into those systems. If the data isn’t available or isn’t aligned with the requirements of the analysis, the technology platform won’t perform as expected. It’s a common expression, but it’s absolutely true that “bad data in equals bad data out.”

For example, a cloud-based data analytics suite typically requires a large upfront investment for the initial implementation as well as recurring charges based on the number of users. Regardless of the price, the tool is basically an empty vessel until the right data is collected, stored, analyzed and reported.

Value-based models add responsibilities

Today’s value-based models demand even higher data fidelity across the connected healthcare ecosystem. As providers, payers, and community-based organizations work together on optimizing outcomes, patient data needs to serve multiple purposes.

Clinicians leverage data first and foremost for direct patient care, but data also has to be aggregated effectively to feed into analytical systems that support population health initiatives. Further, the practice or provider system must be able to use that same data to produce quality reports, which in turn must be aligned with the payment mechanisms in place to ensure the enterprise receives maximum reimbursement for its high-quality clinical care.

What you do today in your world with your technology systems increasingly will impact the clinic down the street or the specialist who is providing services to your patients. Value-based care models are connecting new information, new partners, and new insights that go well beyond the point of care.

When looking at the entire ecosystem through the lens of value-based care delivery, leaders must also think about data analysis as a function of risk management. The fundamental shift in incentives brought about by alternative payment arrangements means delivery systems need to have predictive capabilities, heading off costs and utilization proactively.

Care teams are using technology to address social determinants of health, for example. Hospitals are spending billions to address food security, transportation, education, and other factors that influence health outcomes. Having insight into the drivers of poor health as well as the connections to solve them helps providers improve population health and share in the gains from their value-based arrangements. It all goes back to the way humans interact with the tech tools that support their processes.

Build your technology strategy purposefully with the big picture in mind, not just for today but for tomorrow as well.

Canton & Company’s multidisciplinary team of healthcare technology leaders, operations experts, and clinical professionals will help you design a new target operating model for the emerging healthcare market, whether it’s a fresh take on technology, a consolidation, or a new strategic initiative. Start a conversation with the Canton team today!

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