Innovating Without Waste: The Case for a Patient-Centered Approach to Data Use in Healthcare

As healthcare providers continue to refine their data strategies, it’s imperative they put patient outcomes and experiences first.
May 1, 2024
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With more and more data related to our health available every day, and with still more new ways that data is being collected and analyzed, opportunities abound for innovation when it comes to data use in healthcare. 

As a growing number of healthcare payers and providers make the leap from legacy or on-premise data systems into centralized platforms like the Snowflake Data Cloud, new capabilities and AI-driven interventions are also closer at hand than ever before. 

According to findings from Hakkoda’s Healthcare State of Data report, healthcare organizations are also regularly seeing strong returns on their data investments, with an average data technology ROI of 124%. 

Even with all of this innovation at hand, however, the pinnacle of good healthcare is still about putting the patient first, which means we must thoroughly consider the complexities around innovation that can sometimes lead to research and development waste.

Ten years ago, when data was still only a small piece of the puzzle, we were asking questions like “what can we do with our data?” and “what other data can we get?” Now, the questions we most critically need to consider are “how do we prioritize the innovations we’re making with our data?” and “how do you decide what truly matters and pursue it with the least waste?” 

In this blog, we will explore the ways healthcare organizations define quality of care and operational costs. We will then walk through how your organization can measure the value of a given data product, either during or before production, and use this evaluation to drive better returns on its data technology investments. 

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The Bottom Line: How Quality and Cost Inform Data Use in Healthcare

To effectively use data to improve intervention strategies in health care systems, we must understand what is meant by value and the merits of different definitions. One straightforward way of measuring value in healthcare is to weigh the balance of quality and cost, but the challenge goes much deeper than that. Quality can be defined in terms of outcomes, harms and benefits. Similarly, the seemingly simple idea of “cost” gets more complicated when you consider questions like:

  • Cost to whom?
  • Costs over which time period?
  • Cost of what – supplies and/or services?

Given this variability, one must be precise in operationalizing value metrics to drive healthcare innovation. Typically, analysts evaluate medical products based on the value they deliver to a healthcare organization and to its patients. This process takes into consideration product costs, patient outcomes, total cost of care and other data sources to determine if a product will provide value in the form of better outcomes for the patient or lower costs. But to what degree of value are you trying to achieve? For example, does eliminating low value services ultimately increase high value care?

One must also consider cross functional areas of value evaluation. Using an attribution model, what revenue uplift or positive patient outcome impact can the data product contribute per function – how does the data team help the marketing, sales and customer success teams to generate more revenue? 

As you can see, the task of assigning and measuring value to healthcare products can snowball, quickly. To simplify, here are the two key ways to impactfully measure data product value, depending on if the assessment is done during production or before it begins.

Calculating the Value of a Data Product in Production

Calculating the value of a data product in healthcare involves considering several factors, including the impact it has on patient outcomes, operational efficiency, cost savings, and overall healthcare delivery. Here’s a step-by-step guide on how to approach this:

  1. Define the Objectives: Clearly outline the goals and objectives of the data product. What problem is it solving? What outcomes are you aiming to achieve? For example, reducing readmission rates, improving diagnostic accuracy, or optimizing resource allocation.
  2. Identify Key Metrics: Determine the key performance indicators (KPIs) that will measure the success of the data product. These metrics could include patient outcomes (e.g., mortality rates, complication rates), operational metrics (e.g., length of stay, wait times), financial metrics (e.g., cost savings, revenue generation), and user satisfaction.
  3. Gather the Data: Collect relevant data sources that will be used to evaluate the impact of the data product. This may include clinical data (e.g., electronic health records, lab results), operational data (e.g., staffing levels, resource utilization), and financial data (e.g., reimbursement rates, cost of care).
  4. Baseline Assessment: Establish a baseline for the identified metrics before implementing the data product. This provides a reference point for comparison and helps quantify the impact of the intervention. 
  5. Implementation and Monitoring: Deploy the data product and closely monitor its performance. Track the identified metrics over time to assess its effectiveness and identify areas for improvement.
  6. Quantify Impact: Analyze the data collected post-implementation to quantify the impact of the data product. Compare the outcomes and metrics with the baseline to determine any improvements or changes attributable to the intervention.
  7. Monetary Valuation: Translate the improvements in metrics into monetary terms wherever possible. This could include estimating cost savings (e.g., reduced hospital readmissions, fewer diagnostic tests), revenue generation (e.g., increased patient volume, reimbursement rates), or efficiency gains (e.g., reduced staff hours, streamlined workflows).
  8. Cost-Benefit Analysis: Compare the costs associated with developing, implementing, and maintaining the data product against the monetary value of the benefits it generates. Calculate the return on investment (ROI) to assess whether the data product is providing sufficient value relative to its costs.
  9. Qualitative Assessment: In addition to quantitative metrics, consider qualitative factors such as improved patient experience, clinician satisfaction, and organizational reputation when evaluating the value of the data product.
  10. Iterative Improvement: Continuously assess and refine the data product based on feedback and performance metrics. Iterate on the solution to further enhance its value over time.


By following these steps, you can systematically evaluate the value of a data product in healthcare and make informed decisions about its implementation and optimization.

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Predicting the Value of a Data Product Before Production

In other cases, we haven’t built anything yet and we want to measure the value of what we plan to build or use value assessments to prioritize development work. Measuring the value of a data product before building it involves estimating its potential impact on various aspects such as patient outcomes, operational efficiency, and financial performance. While there isn’t a one-size-fits-all formula, you can create a framework that combines multiple factors to evaluate the potential value proposition. Here’s a basic formula that you can adapt to your specific context:

Value = Benefit − Cost

Benefit: Estimate the potential benefits that the data product could provide. This can include factors such as:

  • Improved Patient Outcomes: Estimate the potential impact on patient health and well-being, such as reduced mortality rates, improved clinical outcomes, and enhanced quality of life.
  • Cost Savings: Estimate the potential cost savings resulting from efficiency gains, reduced waste, and avoidance of adverse events. This could include savings in staffing costs, inventory management, and avoidable treatments or procedures.
  • Revenue Generation: If applicable, consider potential revenue opportunities resulting from increased patient volume, expanded service offerings, or improved billing and reimbursement practices.
  • Competitive Advantage: Evaluate the potential for the data product to provide a competitive edge, such as differentiation in the market, enhanced brand reputation, or increased customer loyalty.
  • Operational Efficiency: Assess the potential improvements in operational processes, such as reduced wait times, increased throughput, optimized resource allocation, and streamlined workflows.

Cost: Estimate the costs associated with developing, implementing, and maintaining the data product. This can include factors such as:

  • Development Costs: Estimate the expenses related to software development, data acquisition, algorithm design, and testing.
  • Implementation Costs: Estimate the expenses associated with deploying the data product, such as training, infrastructure upgrades, and integration with existing systems.
  • Maintenance Costs: Estimate the ongoing expenses required to support and maintain the data product, including software updates, data management, and technical support.
  • Opportunity Costs: Consider any potential trade-offs or missed opportunities resulting from allocating resources to develop the data product instead of alternative investments or initiatives.

Once you have estimated the potential benefits and costs, you can subtract the total expected costs from the total expected benefits to calculate the expected value of the data product. Keep in mind that this formula provides a simplified framework and may need to be adjusted based on the specific context, objectives, and stakeholders involved. Additionally, it’s essential to conduct thorough research, analysis, and validation to ensure the accuracy and reliability of the estimates used in the calculation.

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Driving More Strategic Data Use in Healthcare with Hakkōda

Innovation is at the heart of healthcare transformation, and data products play a pivotal role in driving this change. By effectively measuring the value of these innovations, healthcare organizations can make informed decisions, allocate resources efficiently, and ultimately improve patient care. It’s essential to adopt a comprehensive approach that considers clinical, operational, financial, and patient-centered outcomes. As the healthcare landscape continues to evolve, embracing innovation and leveraging data-driven value equations will be critical for delivering high-quality, accessible, and equitable care to all.

Hakkoda is committed to delivering high quality solutions without unnecessary waste. By using the strategies listed above, we are able to give our customers a unique experience that focuses on improving patient outcomes and balances data and resource costs and benefits. Our healthcare consultants also have experience in the customer seat at large healthcare providers, and understand the unique challenges and complexities that surround healthcare data strategy, from working with proprietary EHR data, to maintaining compliance with strict legal and security requirements, to transforming patient outcomes with cutting-edge AI and machine learning technology. Bringing that experience together with first-rate expertise across the modern tech stack, our teams understand your data and business needs and have the skills you need to achieve them. 

Ready to join innovators in your field by reducing data product waste and driving stronger returns on investment with a winning data strategy? Let’s talk

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