This is Part 3 of the Symptoms to Sources series, drawn from Test Double's original research report, Symptoms to Sources: Biggest Software Challenges for Healthcare in 2026.
- Healthcare software teams keep fixing symptoms: Real problems run deeper
- Legacy systems and delivery pressure: Symptoms healthcare tech teams keep treating
- You are here: The root causes healthcare teams overlook: Siloed data and misaligned priorities
- How to shift focus from symptoms to build stronger healthcare software systems
Operational issues don't stem from the symptoms we discussed in Part 2—tech debt, legacy rewrites, and delivery pressure—although the effects are problematic, painful, and costly. They trace back to deeper system and process gaps.
Lasting improvement comes from identifying and addressing these root issues. Healthcare organizations need to tackle the cause rather than the consequence.
In our experience, the root cause of so many technical and product problems falls into three categories.
1. Siloed data across healthcare systems and EHRs
Across the healthcare industry and the broader software space, interoperability and data standardization are near-universal struggles.
Patient information often lives in separate systems—EHRs, lab databases, insurance portals, and specialty care platforms—that don't communicate with each other. Without a single, straightforward way to share data between systems, products, and the providers, clinicians, and patients they support, a complete picture of care stays out of reach.
Service providers and healthcare SaaS companies contend with standards that vary from facility to facility, supported by government entities. But the protocols and frameworks used to implement those standards vary widely. The data under the hood is inconsistent at best and messy and unreliable at worst.
"Structured data and integration between other health entities, even getting data out to other vendors are some of the most consistent problems we've dealt with. There are structured systems out there that nobody actually utilizes in the correct way. It's a systemic problem with no true governing authority of how data should be transferred back and forth."
— Andrew Warner, VP of Product at Genome Medical
Data siloes are complex, multifaceted root issues that require deeper solutions—from new tech infrastructure to new engineering processes and methodologies.
How data siloes impact providers, engineering teams, and patients
For healthcare providers, data siloes mean spending time reconciling records and manually transferring information—increasing the risk of error and pulling valuable time away from patient care.
For engineering and product teams, siloes mean time spent normalizing data, building complex integrations, and maintaining compliance—time that could go toward higher-value initiatives.
For patients, siloes are more than an inconvenience. Patients find themselves constantly repeating their medical history, retaking tests, and fighting for conclusive answers. Over time, these frustrations lead patients to lose faith in the medical system's support for their holistic health and wellness.
2. Wrong metrics driving healthcare product and engineering teams
Many product and engineering teams are incentivized to deliver new features or complete tasks quickly, emphasizing speed over value. A culture that prizes task completion loses sight of the problems it aims to solve and the value it brings to customers.
The easily accessible metrics rarely reflect the bigger picture.
When product teams are applauded for simply delivering a feature, there's less accountability for whether the system or product is actually solving a root cause and generating value—or who is accountable for its maintenance and long-term success. This leads to greater tech debt, product underperformance, inefficiency, and reduced profitability.
"The available data can influence what we measure and therefore, drive the objectives we focus on. But, if our KPIs don't accurately reflect business value, then how do you know if you're driving to the wrong objective?"
— Kiley Blake, KODE Health
Product and engineering teams' measurement strategies must be grounded in both business goals and the voice of the customer. In healthcare, "revenue" might be the desired business outcome, but the target metric must always be bigger. To address wider issues, tech teams need to measure incremental improvements in customers' well-being—access to health information, more convenient care, and more collaborative interactions with providers.
3. Strategic misalignment between healthcare leadership and tech teams
The right prioritization generates business impact. Misalignment derails progress.
In health IT, outcomes depend on coordination across departments, vendors, and regulatory frameworks. When leadership, product, and technical teams operate from different interpretations of priorities, goals compete with one another and everyone wastes time.
Goals that aren't explicit from leadership become implied, and misalignment tends to hide behind busy roadmaps. Well-intentioned teams chase conflicting priorities, missing opportunities to improve patient outcomes and product returns. Legacy systems don't move as fast as the business needs.
Clearly defined outcomes, transparent communication, and shared accountability get teams unstuck.
"So much of business is human. Product leaders need to build relationships, have candid conversations, back claims up with data, and keep driving toward the bigger goal," said Blake.
"I've seen product leaders get too hyper-focused on a KPI or an outcome to the point where it becomes all-consuming. You must be clear on what the business is driving toward, but remain flexible enough to adapt with shifts in the business and competitive market. You need to empower your team to put the right measurements in place to make good decisions and bring some pragmatism to the table."
— Kiley Blake, KODE Health
By rallying teams around clear outcomes, everyone remains focused and accountable. They share the same definition of success, move faster, make smarter tradeoffs, share learnings, and deliver better results for patients and the business.
"It's a constant balancing act of understanding the needs of the business, customer, and your IT partners. We have worked very diligently to be better partners, sit at the table with our operational teams, and bring that voice of the customer to the problems we're collectively working on, all within the context of the organization's most important goals."
— Sara Saldoff, OhioHealth
Frequently asked questions about root causes of healthcare software problems
How do you break down data siloes in healthcare when you don't control the EHR?
Most healthcare product and engineering teams don't own the EHR—they build around it. The practical approach is to focus on what you can control: the integration layer. Build a well-governed data abstraction layer that normalizes data from multiple sources (EHRs, lab systems, insurance portals, specialty platforms) into a consistent internal model. Invest in FHIR-based APIs where possible, but plan for proprietary connections where necessary—because roughly two-thirds of hospital API connections still use proprietary standards. The organizational side matters just as much as the technical side: establish clear data ownership, define data quality standards your team enforces at the integration boundary, and build relationships with the clinical and operations teams who understand how data flows through the systems you're connecting to.
What metrics should healthcare product teams track instead of feature delivery velocity?
The shift is from measuring what you ship to measuring what changes for the customer. For healthcare products, meaningful metrics often include time-to-task-completion for clinical workflows (are clinicians spending less time on data entry?), patient portal engagement depth (not just logins, but actions taken beyond the initial task), claim processing cycle time, prior-authorization approval rates, and patient-reported experience scores. The key is tying your team's metrics to a business outcome your leadership cares about—then measuring leading indicators that your team can directly influence. If leadership cares about efficiency, measure the specific workflow improvements that drive efficiency, not the number of features released.
How do you get leadership alignment when different executives define success differently?
This is one of the most common and most damaging patterns we see. The fix starts with making implicit goals explicit. Run a structured alignment exercise—something as simple as asking each executive to independently write down the top three outcomes they expect from a technology initiative, then comparing answers in the same room. The gaps are usually revealing and productive. From there, agree on a single North Star metric that everyone can rally around, with supporting metrics that map to each function's contribution. The North Star doesn't resolve all disagreements, but it creates a shared reference point for making tradeoffs. When priorities conflict (and they will), the team can ask: "Which option moves the North Star metric more?" instead of defaulting to whoever has the most organizational power.
Why do healthcare organizations keep repeating the same modernization cycle every few years?
Because most modernization efforts treat the technology as the problem and stop there. The team updates the system, declares victory, and goes back to the same processes, communication patterns, and measurement frameworks that created the tech debt in the first place. Within two to three years, the "new" system has accumulated its own debt and the cycle restarts. Breaking the loop requires changing the organizational systems alongside the technical ones: how teams prioritize work, how they measure success, how they communicate across functions, and how they make decisions about when to invest in maintenance versus new features. If the only thing that changes is the code, the underlying conditions that produced the problem are still in place.
What comes next in this series
Recognizing these root causes is the critical first step. But recognition alone doesn't fix anything.
In Part 4—the final article in this series—we walk through a holistic framework for solving these problems at the source: defining your North Star, investing in outcomes over predetermined projects, and choosing the right external partner. The goal is to help your teams stop cycling through the same problems and start building toward meaningful, sustainable outcomes.
For technology leaders at inflection points—navigating a major modernization, an AI bet, or a scaling decision—these three root causes are exactly why those decisions feel so high-stakes. When the underlying data is siloed, the metrics are wrong, and the teams aren't aligned, every investment carries more risk than it has to.
- Healthcare software teams keep fixing symptoms: Real problems run deeper
- Legacy systems and delivery pressure: Symptoms healthcare tech teams keep treating
- You are here: The root causes healthcare teams overlook: Siloed data and misaligned priorities
- How to shift focus from symptoms to build stronger healthcare software systems
Read the full report: Symptoms to Sources: Biggest Software Challenges for Healthcare in 2026
Know a leader in healthcare who cares about these things? Share the survey with them so we can expand our findings.
