HJBR May/Jun 2026

HEALTHCARE JOURNAL OF BATON ROUGE I  MAY / JUN 2026 23 consideration given to discontinuing it due to theoretical concerns about yeast infections — without any mention of its well-established re- nal and cardiovascular benefits. This scenario reflects a system in which criti- cal, evidence-based interventions depend on individual recall rather than reliable execution. This is not a failure of effort or intent; it is a fail- ure of system design. This is not an abstract systems problem — it is measured in missed opportunities, delayed therapies, preventable complications, and lives shortened not by lack of knowledge, but by failure to reliably apply it. A true system of action would not leave these critical decisions to variable recall or compet- ing priorities during a busy clinic day. It would identify the patient in real time, synthesize the relevant data, and surface a clear, prioritized plan — highlighting care gaps, contextualizing risks and benefits, and reinforcing evidence- based recommendations at the point of care. In such an environment, innovation would shift toward a fundamentally different frontier — one in which technology is continuously re- fined to help people live longer, fuller, higher- quality lives. The focus would move away from the routine deployment of interventions that are ineffective or only marginally beneficial, and toward a system that consistently elevates what works, scales it rapidly, and aligns care with outcomes that truly matter to patients. The Real Problem: Misaligned Incentives, Not Missing Technology At its core, the problem is not that healthcare lacks technology; it is that it lacks alignment. This misalignment is caused by a fundamental design flaw: Healthcare is organized around encounters, not individuals. We optimize visits, document visits, and bill for visits, but health does not improve in visits; it improves over time. The prevailing payment model continues to reward volume over value, activity over out- comes, and documentation over decision-mak- ing. In such a system, it should not be surprising that the dominant technology platforms have evolved to optimize for those same priorities. The electronic medical record is exceptionally good at supporting billing workflows, captur- ing documentation to justify levels of service, ensuring regulatory compliance, and — per- haps unintentionally — perpetuating wide, clinically unwarranted variation in how care is delivered from one clinician to the next. It is far less effective at supporting clinical reasoning, longitudinal care, or the reliable execution of evidence-based workflows. In other industries, high-performing systems reduce unwarranted variation through stan- dardized, algorithmic processes that consis- tently produce reliable and predictable out- comes. In healthcare, by contrast, our core technology platforms largely preserve variabil- ity rather than constrain it, reflecting a system designed around the status quo rather than one relentlessly focused on improving health and health outcomes. In many ways, the EMR is not a failed product; it is a highly successful reflection of the system it was built to serve. We cannot simply digitize our way out of a structural problem. The addition of more alerts does not inherently produce better care, just as more data does not guarantee better deci- sions, and more documentation does not trans- late into better outcomes. In fact, when layered onto a misaligned system, these “enhance- ments” often compound the very inefficiencies they are meant to solve, adding noise, increas- ing cognitive burden, and further distancing clinicians from meaningful clinical work. History offers a useful parallel. The transpor- tation revolution did not occur because we built faster horses; it required a fundamental rethinking of how people and goods moved from one place to another. Likewise, the mod- ern automotive industry did not emerge by op- timizing a broken assembly line; it required a redesign of the entire production system, from inputs to outputs. Healthcare now finds itself at a similar crossroads. As long as the underlying architecture remains unchanged, technology will continue to amplify existing flaws rather than resolve them, creating the illusion of prog- ress while leaving the core problems intact. A truly redesigned system would not begin with the question, “How do we document this visit?” but rather, “How do we improve this pa- tient’s health over time?” That distinction is not semantic — it is foundational. It shifts the unit of care from the encounter to the individual, from episodic transactions to continuous lon- gitudinal relationships, and from reactive treat- ment to proactive management. And yet, for all of its shortcomings, tech- nology may still hold the key to healthcare’s transformation — not in its current form, but in what it is becoming. If thoughtfully designed and properly aligned with a system that values outcomes over activity, the next generation of tools has the potential to do what current sys- tems cannot: reduce cognitive burden, synthe- size complexity, and restore the centrality of the patient–physician relationship. The promise of technology in healthcare has not been fulfilled — but it also has not been exhausted. What lies ahead is not simply better software, but a fundamentally different way of delivering care. And it is there — at the inter- section of redesign and emerging capability — that the next chapter in the future of healthcare will be written, and its title might very well be “The Dawn of AI.” n REFERENCES B. G. Arndt et al, “Tethered to the EHR: Primary Care Physician Workload Assessment Using EHR Event Log Data and Time-Motion Observations,” Annals of Family Medicine 15, no. 5 (2017): 419– 426. C. Sinsky et al., “Allocation of Physician Time in Ambulatory Practice: A Time and Motion Study in 4 Specialties,” Annals of Internal Medicine 165, no. 11 (2016):753–760. Kidney Disease: Improving Global Outcomes (KDIGO) CKD Work Group, “KDIGO 2024 Clinical Practice Guideline for the Evaluation and Management of Chronic Kidney Disease,” Kidney International 105, no. 4S (2024): S117–S314. L. P. Gregg et al., “Predictors, Disparities, and Facility-Level Variation: SGLT2 Inhibitor Prescription Among US Veterans with CKD,” American Journal of Kidney Diseases 82, no. 1 (2023): 53–62.e1. National Academies of Sciences, Engineering, and Medicine. “Taking Action Against Clinician Burnout: A Systems Approach to Professional Well-Being” (National Academies Press, 2019), https://doi.org/10.17226/25521. “Physician Burnout & Wellness: Overview and Evidence-Based Strategies,” American Medical Association, updated 2023, https://www.ama - assn.org/practice-management/physician- health/physician-burnout (page no longer available). S. J. Jeong et al., “Barriers to Initiating SGLT2 Inhibitors in Diabetic Kidney Disease: A Real- World Study,” BMC Nephrology 22, no. 1 (2021): 177. T. D. Shanafelt et al., “Relationship Between Clerical Burden and Characteristics of the Electronic Environment with Physician Burnout and Professional Satisfaction,” Mayo Clinic Proceedings 91, no. 7 (2016): 836–848, https://10.1016/j.mayocp.2016.05.00.