Exploring Human Potential

The AI Enhanced Personal Health Record – The Key That Unlocks The Door To Universal Health Care.

Posted on | April 23, 2024 | Comments Off on The AI Enhanced Personal Health Record – The Key That Unlocks The Door To Universal Health Care.

Mike Magee

In a system that controls 1/5 of the U.S. GDP; one that in 2017 employed 16 non-clinical workers for every physician; and one that under-performs at every turn (most notably for women and children, the poor, and people of color); one would be hard pressed to identify a better target for AI-driven national reform.

Pessimists say we’ve been down  this way before and that the various arms of the Medical Industrial Complex will place enough road blocks in the way to slow down this transforming steam roller.

But optimists suggest that this time is different, and that the entry of generative Artificial Intelligence (or “Augmented Intelligence” – the AMA’s preferred term for AI) is, in fact, a real game changer –  and that your Personal Health Record is the key that unlocks the door.

Surveys  show that 8 in 10 health care execs already use generative AI in some form, and that 2/3 of physicians see advantages for them and their patients. From clerical to clinical to discovery, opportunity abounds. A technology that can self-correct its own errors and is easy enough to use that health professionals and the people they care for start on an even playing field sounds like a “safe bet.”

But seasoned health reformers increasingly point to a third factor – the infrastructure already in place with Electronic Health Records (EHRs), and the knowledge and connectivity we’ve built as we’ve overcome obstacles over the past three decades. Consider, they say, where we have been, and how far we have come.

In my father’s day, and throughout much of my own training, paper “patient charts” ruled the day. As I began my surgical training in 1973, the value of electronic health records (EHRs) was still largely theoretical, and its usefulness was largely defined as the capacity to finally ensure that physician hand writing was legible.

In the first two decades of experimentation with various hybrid forms of EHRs the focus was on hospital billing and scheduling systems supported by large mainframe computers with wired terminals and limited storage. The notion of physician entry was seen as largely impractical both on behavioral and financial grounds. By 1990, early medical IT dreamers were imagining a conversion as personal computing emerged (“affordable, powerful, and compact”) fed by data flowing over the Internet.

In 1992, the effort received a giant boost from the Institute of Medicine which formally recommended a conversion over time from a paper-based to and electronic data system. While the sparks of that dream flickered, fanned by “true-believers who gathered for the launch of the International Medical Informatics Association (IMIA), hospital administrators dampened the flames siting conversion costs, unruly physicians, demands for customization, liability, and fears of generalized workplace disruption.

True believers and tinkerers chipped away on a local level. The personal computer, increasing Internet speed, local area networks, and niceties like an electronic “mouse” to negotiate new drop-down menus, alert buttons, pop-up lists, and scrolling from one list to another, slowly began to convert physicians and nurses who were not “fixed” in their opposition.

On the administrative side, obvious advantages in claims processing and document capture fueled investment behind closed doors. And entrepreneurs were already predicting that “data would be king” in the future. If you could eliminate filing and retrieval of charts, photocopying, and delays in care, there had to be savings to fuel future investments. 

What if physicians had a “workstation,” movement leaders asked in 1992? While many resisted, most physicians couldn’t deny that the data load (results, orders, consults, daily notes, vital signs, article searches) was only going to increase. Shouldn’t we at least begin to explore better ways of managing data flow. Might it even be possible in the future to access a patient’s hospital data in your own private office and post an order without getting a busy floor nurse on the phone?

By the early 1990s, individual specialty locations  in the hospital didn’t wait for general consensus. Administrative computing began to give ground to clinical experimentation using off the shelf and hybrid systems in infection control, radiology, pathology, pharmacy, and laboratory. The movement then began to consider more dynamic nursing unit systems. 

By now, hospitals legal teams were engaged. State laws required that physicians and nurses be held accountable for the accuracy of their chart entries through signature authentication. Electronic signatures began to appear, and this was occurring before regulatory and accrediting agencies had OK’d the practice.

By now medical and public health researchers realized that electronic access to medical records could be extremely useful, but only if the data entry was accurate and timely. Already misinformation was becoming a problem. Whether for research or clinical decision making, partial accuracy was clearly not good enough. Add to this a sudden explosion of offerings of clinical decision support tools which began to appear, initially focused on prescribing safety featuring flags for drug-drug interactions, and drug allergies. Interpretation of lab specimens and flags for abnormal lab results quickly followed. 

As local experiments expanded, the need for standardization became obvious to commercial suppliers of EHRs. In 1992, suppliers and purchasers embraced Health Level Seven (HL7) as “the most practical solution to aggregate ancillary systems like laboratory, microbiology, electrocardiogram, echocardiography, and other results.” At the same time, the National Library of Medicine engaged in the development of a Universal Medical Language System (UMLS).

As health care organizations struggled along with financing and implementation of EHRs, issues of data ownership, privacy, informed consent, general liability, and security began to crop up.  Uneven progress also shed a light on inequities in access and coverage, as well as racially discriminatory algorithms. 

In 1996, the government instituted HIPPA, the Health Information Portability and Accountability Act, which focused protections on your “personally identifiable information” and required health organizations to insure its safety and privacy.

All of these programmatic challenges, as well as continued resistance by physicians jealously guarding “professional privilege, meant that by 2004, only 13% of health care institutions had a fully functioning EHR, and roughly 10% were still wholly dependent on paper records. As laggards struggled to catch-up, mental and behavioral records were incorporated in 2008.

A year later, the federal government weighed in with the 2009 Health Information Technology for Economic and Clinical Health Act (HITECH). It incentivized organizations to invest in and document “EHRs that support ‘meaningful use’ of EHRs”. Importantly, it also included a “stick’ – failure to comply reduced an institution’s rate of Medicare reimbursement.

By 2016, EHRs were rapidly becoming ubiquitous in most communities, not only in hospitals, but also in insurance companies, pharmacies, outpatient offices, long-term care facilities and diagnostic and treatment centers. Order sets, decision trees, direct access to online research data, barcode tracing, voice recognition and more steadily ate away at weaknesses, and justified investment in further refinements.

The health consumer, in the meantime, was rapidly catching up. By 2014, Personal Health Records, was a familiar term. A decade later, they are a common offering in most integrated health care systems.

All of which brings us back to generative AI, and  New multimodal AI entrants, like ChatGPT-4 and Genesis. They will not be starting from scratch, but are building on all the hard fought successes above.

Multimodal, large language, self learning mAI is limited by only one thing – data. And we are literally the source of that data. Access to us – each of us and all of us – is what is missing.

What would you, as one of the 333 million U.S. citizens in the U.S., expect to offer in return for universal health insurance and reliable access to high quality basic health care services?

Would you be willing to provide full and complete de-identified access to all of your vital signs, lab results, diagnoses, external and internal images, treatment schedules, follow-up exams, clinical notes, and genomics?  An answer of “yes” could easily trigger the creation of universal health coverage and access in America.

Two key questions remain:

  1. How will mAI keep up? Answer: Generative AI is self-correcting and self-improving based on data input. Strong regulatory oversight will be essential. But with these protections in place, health coverage in the future will likely require you to provide all your de-identified data in return for access to care and coverage. You are now your data.
  2. How will all that data be stored? Answer: New chips, like those provided by Nvidia modeled after gamer chips originated by Atari, better able to manage the load, but at what cost? Dollars for sure, but also extraordinary consumption of energy and water for cooling.


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