Beyond technology: align AI tools with a systemic change in health care

Two years ago, each health conference I attended had several panels on the professional exhaustion of clinicians. The problem is well known and the system’s real contributors have been called.

During the last year, the professional exhaustion solution has been splashed on each conference application connection screen: generative artificial intelligence (IA).

Labor productivity statistics lead some to proclaim that the United States is on the “cusp of a boom in productivity”. The generalized investment in AI strengthens the optimism of economists about “20 years” of workers productivity on the horizon. However, this will not take place in health care without supporting the systemic and organizational actions that rethink what we incite financially, how we integrate new technologies, the way we change tasks and how we prepare the workforce.

The Scribe AI revolution

In health care, ambient documentation tools have become the star of the show. These “AI scribes” listen to the patient-medicine conversation, transcribe the discussion, then use a generative AI to create a first clinical note. These solutions eliminate laborious work to fully capture the patient’s history or doctor’s reflections on the level. This technology is considered by some as a miracle. Before large -language model chat applications were popularized, many doctors did not think that such solutions were possible during our career.

Doctors of mid-carrier like me started our careers to build models in the electronic health file (DSE) and to type furiously while our patients told us intimate stories about what led to their planned visit. Thus, having created project notes so that we can simply modify and sign resemble a central burden. This is perhaps the reason why the demand for these products has increased dramatically in the past two years. The main medical information officers across the country are exposed (rather, requests!) Of their clinicians to provide such solutions. For many, these tools bring them home earlier for dinner, the decrease in hours after working hours in the DSE and the decrease in cognitive burden that accompanies it to remember all the pieces in the history of a patient between the moment when he hears it and the time he can hit or dictate it in the DSE.

This ambient documentation technology is one of the first solutions that have completed basic end users as a critical tool to support current clinical practice. This contrasts with the last 15 years of DSE and other additional digital modules that have been pushed by leaders or organizational sellers looking for the next unicorn.

The long -awaited unicorn is there and it’s real.

Contradictory visions for the impact of AI

Organizational leaders are delighted with the future AI can potentially create. There are endless administrative ineffectiveness that affect patient care or swell costs. Work represents almost 60% of the average spending of the American hospital, according to the American Hospital Association. If there was a way to make clinicians more productive – but it could be measured – then hospital leaders are in it.

However, not everyone is ready to kiss the whole unicorn without first understanding where the unicorns come from.

First -line clinicians are still pretty suspicious. Their concerns arise from technology itself and also what organizing or system leaders will do with new efficiency drawn from the deployment of such technology. These next steps – policy changes and operational actions that follow large implementations of AI scribe – are the critical elements that will determine its success.

Will organizations simply continue to add more patients and more tasks to the plates of doctors? Are we going to slide generative AI solutions in existing clinic processes that do not serve the ideal workflow of clinicians? Are we going to integrate these generative AI tools into DSE systems that do not support the thought models of doctors or the desired narrative ends?

Or, now that we have confidence in the performance of AI documentation solutions, are we going to step back and rethink the current paradigm of the way a clinician spends his time? Now that I know that the initial note of the project will be quite precise, can I spend more time focusing on the difficult history of a patient, diving more deeply into the deep causes of health problems, instead of clicking on the keyboard to perform data database tasks while talking? Can I take more efforts in my patient schedule, so that I stay on time for the following patients, that I keep a reasonable “time of lunch” and that I have a lot of cartography which focuses on the communication of complex reasoning behind my diagnostics and my treatment decisions?

Systemic solutions to maximize the potential of AI

These are all decisions and interventions at the level of the system where the magic of the AI ​​will be carried out. Policies that move payment models to encourage value and improve health results could reward quality time with patients compared to the additional visit volume. The redesigning efforts of organizational care can ensure that multidisciplinary teams use the best of AI to make the top of their license to serve their populations most effectively. In higher education, the cooking of AI literacy in fundamental education programs for all health workers can encourage clinicians to use AI to increase their work. The AI ​​can manage low -value tasks – check boxes, data capture, administrative steps – while highly qualified workforce does what they do best: connect and communicate with their patients.

Organizations or managers who do it much better recruit and keep well -trained health workers, will improve responsiveness to their patients and potentially reduce costs for all stakeholders. The health systems that fix the right payment incentives will use gains generated by AI towards the end of better health of the population and, in the end, greater economic and security productivity.

We must take advantage of the current opportunity to ensure this dramatic expansion of the efficiency led by AI and access to information reaches the communities in a way that fills equity differences rather than aggravating disparities.

Finally, research efforts should rigorously study the impact of these solutions on the well-being of clinicians and patients’ results. Without close assessment and solid evidence, we risk implementing solutions that seem promising but fail to provide significant improvements or create involuntary consequences.

The need for a holistic implementation strategy

Yes, the professional exhaustion of doctors has improved. However, having a little less than half of the doctors who feel exhausted are hardly celebrating. The global health workforce does not develop quickly enough to meet the growing demand for health care motivated by demographic and epidemiological changes. Family medicine – A basic primary care specialty that can provide a major return on public health investments – remains unpopular as a career path.

The moment could not be better for a “productivity boom” in health care. AI’s tools are there. Marc Benioff predicted a wave of productivity to come in Ai; The one we are well placed to get into health care. Systemic actions, by drawing new policies and operational levers, will finally determine whether we really capitalize on such revolutionary capacities or if we propagate the engines of the professional exhaustion of clinicians and the health of the mediocre population

Photo: Pixelembargo, Getty Images

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