Improving primary healthcare through AI

This is a HBR post, as usual, makes many presumptions. Incorporating the AI in any facet of healthcare requires “re-engineering” workflows and human resources. However, it adds something of value and is a pointer towards standardised healthcare approaches.

How Algorithms Could Improve Primary Care

Automated clinical algorithms can be as simple as age-based rules that trigger a scheduling call for a preventive vaccination or as complex as an automated clinical pathway specifying a series of tests and treatments for chronic conditions such as high blood pressure. …
The use of automated algorithms to assist with Covid-19 follows a tradition of successful incorporation of decision-support algorithms for conditions such as heart failurediabetes, and anticoagulation, which require ongoing monitoring and frequent medication-dose adjustment. The prescribing clinician sets a therapeutic goal and prescribes a set of steps to achieve that goal. That process is subsequently managed by other clinicians such as a nurses and pharmacists without real-time input from the prescribing physician. These programs are effective due to their clinical focus and well-established guidelines and often reduce cost while maintaining or improving quality.

What happens when patients can’t or don’t want to move towards a “shared experience”? I have no idea. Diabetes management, for example, is a singular focus of many healthcare organisations because of frequent issues with lack of understanding of dietary practices, insulin or OHA’s titration, and chronic debilitations. These add to the complexity and cost of healthcare delivery, especially through “re-admissions”. There might be a role here.

However, the write up veers into “automating” the primary care delviery:

Automated primary care systems also employ algorithms that guide the process of care. They codify the logic of a clinical process through specification of the steps leading from inputs such as patient factors, including diagnoses and biomarkers, to outputs such as recommended medications.

I don’t think its feasible. There are issues around autonomy, healthcare practices, trial data and genesis of the best evidence. Therapeutic evidence stems from observations and best practices, and not crawling through literature. I’d be appalled if they start pushing Twitter data and discussions into “real-time management” because users are “talking about it in the public square”. There are no clear lines here, and automating “best-practices” requires careful calibration before it is codified and “approved” for clinical delivery.

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