Healthcare Data – 2026 Health IT Predictions

As we wrap up another year and get ready for 2026 to begin, it is once again time for everyone’s favorite annual tradition of Health IT Predictions! We reached out to our incredible Healthcare IT Today Community to get their insights on what will happen in the coming year, and boy, did they deliver. We, in fact, got so many responses to our prompt this year that we have had to narrow them down to just the best and most interesting. Check out the community’s predictions down below and be sure to follow along as we share more 2026 Health IT Predictions!

Check out our community’s Healthcare Data predictions:

Jitin Asnaani, Chief Product Officer at Rhapsody
With increasing experimentation around AI-native protocols like MCP (Model Context Protocol) and A2A (Agent-to-Agent), secure data and AI-ready data will have to converge. CIOs and CISOs will clamor for AI-ready data access that actually meets the security and auditability requirements of healthcare. As a result, zero-trust architectures and API governance will shift from buzzwords to board-level imperatives as leaders demand visibility into how, where, and why data moves before allowing AI agents anywhere near clinical workflows.

Ed Chidsey, President of Inovalon’s Insights Business Unit at Inovalon
The rapid expansion of AI will begin to expose the data limitations that exist within the healthcare ecosystem in 2026. While the pace of AI innovation is extraordinary, progress will be constrained by fragmentation across the healthcare ecosystem, spanning labs, pharmacies, and EHRs, where few organizations are able to truly connect these data sources in a meaningful way and therefore aren’t in a position to reap all the benefits.

Because of this, organizations will prioritize data that is longitudinally linked, normalized, and standardized. For AI to be deployed at scale, data quality will matter as much as model quality, and healthcare organizations that can access and develop both will lead the way.

Jay Anders, MD, Chief Medical Officer at Medicomp Systems
Throwing large language models and ambient listening tools at clinical workflows will not solve healthcare’s biggest challenges. These technologies are only as effective as the quality of the data behind them. In 2026, success will depend on pairing generative AI with evidence-based systems that deliver clean, computable, and interoperable data clinicians can trust.

Jeanne Cohen, CEO and Founder at Motive Medical Intelligence
In 2026, the healthcare organizations that stand apart will be those that empower every physician with clear, data-driven insights into their own performance. When clinicians can see how their practice decisions align with evidence-based care standards — and where they can personally improve — they not only change their practice behaviors, but they also rediscover purpose.

This kind of transparency doesn’t just drive better care decisions; it restores confidence, connection, and ultimately the joy in practicing medicine. Physician performance measures are a crucial enabler of value-based care success, helping providers deliver the right care, at the right time, to the right patient.

Jennifer Duff, Executive Vice President and General Manager at Zelta by Merative
By the end of 2026, the landscape of clinical data management for clinical trials will be very different. The “instant study build” will be the new industry standard with new approaches to a couple of key processes that traditionally have much longer timelines:

• AI-Powered Protocol Automation Will See Accelerated Adoption: Many clinical studies will explore AI to automatically interpret protocols and configure study databases, transitioning from manual configuration to intelligent automation
• Risk-Based Validation will Become More Prevalent Alongside Traditional Testing Methods: The industry may increasingly adopt intelligent, risk-focused validation approaches that maintain compliance while reducing timelines, complementing traditional manual testing

These shifts will also redefine the role of human experts. Freed from repetitive manual tasks, clinical data managers will focus on higher-value work: analyzing complex data, uncovering scientific insights, and accelerating the delivery of life-saving therapies.

Frederico Ferreira Braga, Head of Digital at Debiopharm
Oncology could usher in a new era of precision-guided care driven by integrated healthcare IT. Advances in data analytics and AI enable clinicians to interpret genomic profiles, tumor biology, and real-world outcomes more efficiently, thereby personalizing treatment decisions. Beyond this, IT has the potential to enable real-time adaptive care pathways, where treatment plans evolve dynamically as patient data is collected, monitored, and interpreted.

Predictive models could anticipate responses, identify novel therapeutic opportunities, and even suggest mid-course adjustments to therapy, driving smarter clinical trials and accelerating innovation. By connecting insights across research and practice, oncology IT has the power to shift care from reactive cycles to proactive, continuously optimized management. This will ultimately improve patient outcomes while redefining how clinicians make decisions and set new standards in cancer care.

Tom Hawkes, Chief Technology Officer at Strive Health
There is a missed opportunity in healthcare right now for artificial intelligence and data strategy to work in tandem and lay the groundwork for effective generative and agentic AI adoption. The importance of data in ADT systems (admit, discharge, transfer) informs the necessary actions to ensure patients have a smooth transition back home and adequate protections, particularly in relation to social determinants of health. Lab data such as measurements of creatinine, eGFR (estimated glomerular filtration rate), and other kidney function markers are essential for helping care teams better understand disease progression.

Utilizing these data streams will enable earlier outreach to patients, more precise interventions, and better long-term health outcomes. Together, efforts like these that partner with near real-time awareness of both utilization and changes in a patient’s clinical condition position healthcare organizations to unlock AI’s full potential in delivering smarter, more proactive patient care.

Sujay Jadhav, CEO at Verana Health
Following 2025’s increased scrutiny on documentation quality and data provenance, the industry will shift toward structured, bias-reduced, longitudinal datasets that clearly show how data was curated. Regulators, researchers, and payers will expect transparent data lineage and clinically meaningful detail for evidence generation. The focus will move away from broad, inconsistent datasets and toward high-quality, specialty-driven RWD that can reliably support drug development, safety evaluation, and guideline alignment.

Ravi Kapur, Co-Founder and CEO at AutoIVF
The next major shift in fertility will be the move from ‘craft IVF’ to ‘validated IVF,’ where automation, data, and reproducibility, not individual heroics, define quality. Today, outcomes vary widely between embryologists and clinics because key steps are still manual and experience-dependent. Over the next decade, intelligent automation and closed-loop workflows will standardize stages from egg retrieval and preparation to embryo culture and selection, making results more consistent while lowering per-cycle costs.

Clinics that now embrace protocol standardization, instrumented labs, and real-world data collection will be best positioned to validate outcomes, join multicenter studies, and meet emerging expectations for evidence, transparency, and equitable access. Intelligent automation will transform a boutique lab service into a robust manufacturing pipeline capable of supporting population-scale reproductive capacity and up to 2.5 million potential births per month worldwide.

Scott Lundstrom, Senior Healthcare Industry Strategist at OpenText
After years of breaches and opaque practices, patients now expect transparency about how their data is used. In 2026, leading healthcare organizations will treat data visibility and consent as central to the care experience, showing who accesses their records and why. Privacy-preserving technologies like federated learning will let patients contribute to innovation without losing control. The systems that empower patients to co-own their healthcare data will set a new standard for trust and loyalty.

Adam Mariano, General Manager, Healthcare at LexisNexis Risk Solutions
As the healthcare industry continues to advance toward seamless data exchange and patient-centered digital access, identity management is a critical cornerstone of trust, privacy, and security. However, legacy identity frameworks were not built for today’s patient-driven environment, and healthcare remains a prime target for misuse of consumer data. The rise of sophisticated identity theft schemes, fueled by the availability of demographic data, forged documents on the dark web, and generative AI, has made it easier for bad actors to bypass traditional identity verification methods.

In 2026, industry leaders will increasingly adopt digital and AI-driven solutions that seamlessly manage the full consumer identity lifecycle, minimizing patient burden and reducing complexity, all without disrupting care. These advanced tools will play a vital role in streamlining service access, enabling expanded virtual care modalities, increasing healthcare data portability for consumers, and opening the door to integrated digital care. Their growing usage will help garner greater trust from patients, create additional opportunities for equitable care, and create the promise of a balance between security and accessibility.

Kim Mclean Boericke, Chief Operations Officer at Veristat
In 2026, AI will be implemented across research biometrics and data operations to improve data integrity. Clinical trials are all about data, and the responsible use of AI in biometrics and data operations represents a major back-office opportunity that will emerge in 2026. By automating data cleaning, anomaly detection, and statistical modeling, AI can increase the speed, insights, and precision of clinical trial data analysis and submission assembly. These efficiencies help shorten development timelines and improve data integrity.

Ultimately, AI will not eliminate the human element but will be the tool that amplifies it. The leaders in this next phase of clinical research will be those who use AI to inform decisions, not replace judgment, ensuring smarter trial design, faster activation, and more reliable data across the clinical lifecycle.

Chris Michalak, Executive Chairman at Personify Health
In 2026, AI will be moving from early innovation to more significant use cases and applications. We will see amazing progress, but will also hit roadblocks along the way.

We will learn that data is the lifeblood of AI and that we all need to collaborate to make that data available to feed the AI solutions that will make healthcare better. We will experience hallucinations in some cases, but great breakthroughs in others. I am confident that claims will be processed faster, providers will have more time with patients, and patients will have better insights about their care. It is not a matter of whether we will make great progress, but how much and how soon.

Lisa Moneymaker, Chief Strategy Officer at Medidata
In 2026, we’ll see a fundamental transformation in how clinical trials are conceived and executed, driven by the unprecedented volume of clinical, operational, and real-world data now available. This expanding data foundation is fueling a new generation of AI-enabled simulation tools that allow sponsors to model trials end to end, test assumptions, forecast bottlenecks, and refine design parameters with data-driven precision before a single patient is enrolled.

This proactive approach will reduce costly amendments, accelerate timelines, and dramatically improve success rates. The ability to simulate before starting will redefine how the industry manages risk, turning clinical development into a predictive, insight-led discipline rather than a reactive process. Ultimately, this shift will allow organizations to bring therapies to market faster and with greater confidence in their outcomes.

John Nash, Vice President, Strategic Initiatives at Redpoint Global
For the year ahead, providers are aiming to push their patient engagement initiatives to be hyper-personalized. That means moving beyond engagement framed around broad segmentation (gender, age, condition) to deliver hyper-personalized support, content, and care interactions tailored to an individual’s real-time needs, behaviors, and preferences. However, to achieve this level of personalization, they must ensure their data is ready for technology integration.

Data readiness means the data they’re working with is complete, with unified profiles that consolidate all health and demographic data; accurate, with cleansed, matched, and accurate household information; timely, so profiles continually update and attribute; actionable, with the right data pieces for the right form for consumption; trusted, with observable and granular match rules; and importantly, compliant.

This data readiness approach clears a path for advancing to hyper-personalization by continually improving upon the accuracy of enterprise data, making no assumptions that data will be fit for purpose and made ready for business use somewhere downstream, or by a third-party vendor.

Kurt Rohloff, Co-Founder and CTO at Duality Technologies
Local Control, Global Intelligence Governments and health systems will assert control over where patient data resides, how it’s processed, and who can access it for AI development, yet this won’t mean closing borders to innovation. Instead, leading healthcare organizations will deploy privacy-enhancing technologies that allow clinical insights and AI model improvements to flow across jurisdictions while patient records remain firmly anchored within their country of origin.

This will enable global healthcare AI systems to learn from distributed datasets spanning multiple nations, all without a single medical record crossing borders. The healthcare AI winners will be those who master this balance: delivering the collaborative power of worldwide data while giving each nation complete authority over its citizens’ health information.

Deepak Singh, Chief Innovation Officer at Adeptia
The era of simple data movement is ending. Across health, life, and other insurance lines, insurers will recognize that integration without intelligence can’t keep up with the complexity of eligibility rules, plan variations, and regulatory demands. Rather than relying on traditional iPaaS tools that simply shuttle files around, insurers will shift toward platforms that can understand, validate, and act on data in real time, reducing rework, compliance risk, and IT dependency while strengthening the entire enrollment and policy administration lifecycle.

Thank you so much to everyone who took the time out of their day to submit a prediction to us, and thank you to all of you for taking the time to read this article! We could not do this without all of your support. What do you think will happen for Healthcare Data in 2026? Let us know on social media. We’d love to hear from all of you!

Be sure to check out all of Healthcare IT Today’s Healthcare Data content and our other 2026 Health IT Predictions.

As we wrap up another year and get ready for 2026 to begin, it is once again time for everyone’s favorite annual tradition of Health IT Predictions! We reached out to our incredible Healthcare IT Today Community to get their insights on what will happen in the coming year, and boy, did they deliver. We, in fact, got so many responses to our prompt this year that we have had to narrow them down to just the best and most interesting. Check out the community’s predictions down below and be sure to follow along as we share more 2026 Health IT Predictions!

Check out our community’s Healthcare Data predictions:

Jitin Asnaani, Chief Product Officer at Rhapsody
With increasing experimentation around AI-native protocols like MCP (Model Context Protocol) and A2A (Agent-to-Agent), secure data and AI-ready data will have to converge. CIOs and CISOs will clamor for AI-ready data access that actually meets the security and auditability requirements of healthcare. As a result, zero-trust architectures and API governance will shift from buzzwords to board-level imperatives as leaders demand visibility into how, where, and why data moves before allowing AI agents anywhere near clinical workflows.

Ed Chidsey, President of Inovalon’s Insights Business Unit at Inovalon
The rapid expansion of AI will begin to expose the data limitations that exist within the healthcare ecosystem in 2026. While the pace of AI innovation is extraordinary, progress will be constrained by fragmentation across the healthcare ecosystem, spanning labs, pharmacies, and EHRs, where few organizations are able to truly connect these data sources in a meaningful way and therefore aren’t in a position to reap all the benefits.

Because of this, organizations will prioritize data that is longitudinally linked, normalized, and standardized. For AI to be deployed at scale, data quality will matter as much as model quality, and healthcare organizations that can access and develop both will lead the way.

Jay Anders, MD, Chief Medical Officer at Medicomp Systems
Throwing large language models and ambient listening tools at clinical workflows will not solve healthcare’s biggest challenges. These technologies are only as effective as the quality of the data behind them. In 2026, success will depend on pairing generative AI with evidence-based systems that deliver clean, computable, and interoperable data clinicians can trust.

Jeanne Cohen, CEO and Founder at Motive Medical Intelligence
In 2026, the healthcare organizations that stand apart will be those that empower every physician with clear, data-driven insights into their own performance. When clinicians can see how their practice decisions align with evidence-based care standards — and where they can personally improve — they not only change their practice behaviors, but they also rediscover purpose.

This kind of transparency doesn’t just drive better care decisions; it restores confidence, connection, and ultimately the joy in practicing medicine. Physician performance measures are a crucial enabler of value-based care success, helping providers deliver the right care, at the right time, to the right patient.

Jennifer Duff, Executive Vice President and General Manager at Zelta by Merative
By the end of 2026, the landscape of clinical data management for clinical trials will be very different. The “instant study build” will be the new industry standard with new approaches to a couple of key processes that traditionally have much longer timelines:

• AI-Powered Protocol Automation Will See Accelerated Adoption: Many clinical studies will explore AI to automatically interpret protocols and configure study databases, transitioning from manual configuration to intelligent automation
• Risk-Based Validation will Become More Prevalent Alongside Traditional Testing Methods: The industry may increasingly adopt intelligent, risk-focused validation approaches that maintain compliance while reducing timelines, complementing traditional manual testing

These shifts will also redefine the role of human experts. Freed from repetitive manual tasks, clinical data managers will focus on higher-value work: analyzing complex data, uncovering scientific insights, and accelerating the delivery of life-saving therapies.

Frederico Ferreira Braga, Head of Digital at Debiopharm
Oncology could usher in a new era of precision-guided care driven by integrated healthcare IT. Advances in data analytics and AI enable clinicians to interpret genomic profiles, tumor biology, and real-world outcomes more efficiently, thereby personalizing treatment decisions. Beyond this, IT has the potential to enable real-time adaptive care pathways, where treatment plans evolve dynamically as patient data is collected, monitored, and interpreted.

Predictive models could anticipate responses, identify novel therapeutic opportunities, and even suggest mid-course adjustments to therapy, driving smarter clinical trials and accelerating innovation. By connecting insights across research and practice, oncology IT has the power to shift care from reactive cycles to proactive, continuously optimized management. This will ultimately improve patient outcomes while redefining how clinicians make decisions and set new standards in cancer care.

Tom Hawkes, Chief Technology Officer at Strive Health
There is a missed opportunity in healthcare right now for artificial intelligence and data strategy to work in tandem and lay the groundwork for effective generative and agentic AI adoption. The importance of data in ADT systems (admit, discharge, transfer) informs the necessary actions to ensure patients have a smooth transition back home and adequate protections, particularly in relation to social determinants of health. Lab data such as measurements of creatinine, eGFR (estimated glomerular filtration rate), and other kidney function markers are essential for helping care teams better understand disease progression.

Utilizing these data streams will enable earlier outreach to patients, more precise interventions, and better long-term health outcomes. Together, efforts like these that partner with near real-time awareness of both utilization and changes in a patient’s clinical condition position healthcare organizations to unlock AI’s full potential in delivering smarter, more proactive patient care.

Sujay Jadhav, CEO at Verana Health
Following 2025’s increased scrutiny on documentation quality and data provenance, the industry will shift toward structured, bias-reduced, longitudinal datasets that clearly show how data was curated. Regulators, researchers, and payers will expect transparent data lineage and clinically meaningful detail for evidence generation. The focus will move away from broad, inconsistent datasets and toward high-quality, specialty-driven RWD that can reliably support drug development, safety evaluation, and guideline alignment.

Ravi Kapur, Co-Founder and CEO at AutoIVF
The next major shift in fertility will be the move from ‘craft IVF’ to ‘validated IVF,’ where automation, data, and reproducibility, not individual heroics, define quality. Today, outcomes vary widely between embryologists and clinics because key steps are still manual and experience-dependent. Over the next decade, intelligent automation and closed-loop workflows will standardize stages from egg retrieval and preparation to embryo culture and selection, making results more consistent while lowering per-cycle costs.

Clinics that now embrace protocol standardization, instrumented labs, and real-world data collection will be best positioned to validate outcomes, join multicenter studies, and meet emerging expectations for evidence, transparency, and equitable access. Intelligent automation will transform a boutique lab service into a robust manufacturing pipeline capable of supporting population-scale reproductive capacity and up to 2.5 million potential births per month worldwide.

Scott Lundstrom, Senior Healthcare Industry Strategist at OpenText
After years of breaches and opaque practices, patients now expect transparency about how their data is used. In 2026, leading healthcare organizations will treat data visibility and consent as central to the care experience, showing who accesses their records and why. Privacy-preserving technologies like federated learning will let patients contribute to innovation without losing control. The systems that empower patients to co-own their healthcare data will set a new standard for trust and loyalty.

Adam Mariano, General Manager, Healthcare at LexisNexis Risk Solutions
As the healthcare industry continues to advance toward seamless data exchange and patient-centered digital access, identity management is a critical cornerstone of trust, privacy, and security. However, legacy identity frameworks were not built for today’s patient-driven environment, and healthcare remains a prime target for misuse of consumer data. The rise of sophisticated identity theft schemes, fueled by the availability of demographic data, forged documents on the dark web, and generative AI, has made it easier for bad actors to bypass traditional identity verification methods.

In 2026, industry leaders will increasingly adopt digital and AI-driven solutions that seamlessly manage the full consumer identity lifecycle, minimizing patient burden and reducing complexity, all without disrupting care. These advanced tools will play a vital role in streamlining service access, enabling expanded virtual care modalities, increasing healthcare data portability for consumers, and opening the door to integrated digital care. Their growing usage will help garner greater trust from patients, create additional opportunities for equitable care, and create the promise of a balance between security and accessibility.

Kim Mclean Boericke, Chief Operations Officer at Veristat
In 2026, AI will be implemented across research biometrics and data operations to improve data integrity. Clinical trials are all about data, and the responsible use of AI in biometrics and data operations represents a major back-office opportunity that will emerge in 2026. By automating data cleaning, anomaly detection, and statistical modeling, AI can increase the speed, insights, and precision of clinical trial data analysis and submission assembly. These efficiencies help shorten development timelines and improve data integrity.

Ultimately, AI will not eliminate the human element but will be the tool that amplifies it. The leaders in this next phase of clinical research will be those who use AI to inform decisions, not replace judgment, ensuring smarter trial design, faster activation, and more reliable data across the clinical lifecycle.

Chris Michalak, Executive Chairman at Personify Health
In 2026, AI will be moving from early innovation to more significant use cases and applications. We will see amazing progress, but will also hit roadblocks along the way.

We will learn that data is the lifeblood of AI and that we all need to collaborate to make that data available to feed the AI solutions that will make healthcare better. We will experience hallucinations in some cases, but great breakthroughs in others. I am confident that claims will be processed faster, providers will have more time with patients, and patients will have better insights about their care. It is not a matter of whether we will make great progress, but how much and how soon.

Lisa Moneymaker, Chief Strategy Officer at Medidata
In 2026, we’ll see a fundamental transformation in how clinical trials are conceived and executed, driven by the unprecedented volume of clinical, operational, and real-world data now available. This expanding data foundation is fueling a new generation of AI-enabled simulation tools that allow sponsors to model trials end to end, test assumptions, forecast bottlenecks, and refine design parameters with data-driven precision before a single patient is enrolled.

This proactive approach will reduce costly amendments, accelerate timelines, and dramatically improve success rates. The ability to simulate before starting will redefine how the industry manages risk, turning clinical development into a predictive, insight-led discipline rather than a reactive process. Ultimately, this shift will allow organizations to bring therapies to market faster and with greater confidence in their outcomes.

John Nash, Vice President, Strategic Initiatives at Redpoint Global
For the year ahead, providers are aiming to push their patient engagement initiatives to be hyper-personalized. That means moving beyond engagement framed around broad segmentation (gender, age, condition) to deliver hyper-personalized support, content, and care interactions tailored to an individual’s real-time needs, behaviors, and preferences. However, to achieve this level of personalization, they must ensure their data is ready for technology integration.

Data readiness means the data they’re working with is complete, with unified profiles that consolidate all health and demographic data; accurate, with cleansed, matched, and accurate household information; timely, so profiles continually update and attribute; actionable, with the right data pieces for the right form for consumption; trusted, with observable and granular match rules; and importantly, compliant.

This data readiness approach clears a path for advancing to hyper-personalization by continually improving upon the accuracy of enterprise data, making no assumptions that data will be fit for purpose and made ready for business use somewhere downstream, or by a third-party vendor.

Kurt Rohloff, Co-Founder and CTO at Duality Technologies
Local Control, Global Intelligence Governments and health systems will assert control over where patient data resides, how it’s processed, and who can access it for AI development, yet this won’t mean closing borders to innovation. Instead, leading healthcare organizations will deploy privacy-enhancing technologies that allow clinical insights and AI model improvements to flow across jurisdictions while patient records remain firmly anchored within their country of origin.

This will enable global healthcare AI systems to learn from distributed datasets spanning multiple nations, all without a single medical record crossing borders. The healthcare AI winners will be those who master this balance: delivering the collaborative power of worldwide data while giving each nation complete authority over its citizens’ health information.

Deepak Singh, Chief Innovation Officer at Adeptia
The era of simple data movement is ending. Across health, life, and other insurance lines, insurers will recognize that integration without intelligence can’t keep up with the complexity of eligibility rules, plan variations, and regulatory demands. Rather than relying on traditional iPaaS tools that simply shuttle files around, insurers will shift toward platforms that can understand, validate, and act on data in real time, reducing rework, compliance risk, and IT dependency while strengthening the entire enrollment and policy administration lifecycle.

Thank you so much to everyone who took the time out of their day to submit a prediction to us, and thank you to all of you for taking the time to read this article! We could not do this without all of your support. What do you think will happen for Healthcare Data in 2026? Let us know on social media. We’d love to hear from all of you!

Be sure to check out all of Healthcare IT Today’s Healthcare Data content and our other 2026 Health IT Predictions.