How Algorithms Can Improve Primary Care

Harvard Business Review published a timely and relevant article entitled “How Algorithms Could Improve Primary Care” authored by Dr. J. Hunter Young, Dr. Kyle Richardville, Bradley Staats, and Dr. Brian J. Miller:

https://hbr.org/2022/05/how-algorithms-could-improve-primary-care

 

In this article, the authors discuss how algorithms can transform primary care, providing automation of the clinical process from simple alerts for refills/vaccinations, patient diagnosis to complex activities such as creating an automated pathway specifying a series of tests and treatments for chronic conditions like high blood pressure. They note that “done well, they [algorithms] enhance operational efficiency and maximize clinical quality”.  Noted directly from the article:

 

“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. At the core of the approach to generating algorithms is a systematic evaluation and synthesis of clinical evidence. Since medical knowledge evolves as evidence accumulates, algorithms must be updated as the evidence and experience with an automated process accumulates. In addition, algorithms may need to consider the feasibility of a range of treatment options. For example, a recommended medication may be too expensive for the patient and therefore other less-expensive options can also be offered. Of course, the trade-offs of selecting an inexpensive option, such as less convenience (e.g., once-daily versus twice-daily dosing), should be specified. Finally, the process of algorithm development and modification requires independent oversight that’s focused on ensuring quality, safety, feasibility, and transparency. For example, a committee composed of an institution’s clinical experts, administrators, and patient representatives might review an algorithm’s impact on patient safety and satisfaction, clinical outcomes, and costs. Delivery systems may also directly partner with software developers, whose algorithms may “crawl” existing medical literature in real time to update guidelines. In turn, software developers should seek out medical professional specialty societies, which are often well-versed in the limitations of existing research.” {emphasis added}

WellAI is already ahead of the curve. The NLP/AI diagnostic engine is the only solution to incorporate more than 30 million medical studies, validated peer-reviewed medical research. The engine has the unique ability to parse the database, which is continually updated, to achieve a rapid and thorough patient diagnosis. It does so in a unique way following the logic of patient questioning versus rules or decision tree expressions. While the solution has not yet achieved the ability to foster complex treatment options, it is on the roadmap – and the solution currently provides courses of action. WellAI’s diagnostic solution is typically prescribed and is under the oversight of physicians and medical practitioners. Additionally, WellAI is working with and has an interest in working with more experts to create specific models and diagnostic tools for diseases and specific health conditions.

 

 

WellAI fits into the front-end or front office clinical workflow where it can reduce the time involved and enhance the precision of initial triage for patient intake or for determining whether a patient requires greater attentiveness. It can also ‘standardize’ patient triage and form a diagnostic baseline – enabling junior practitioners, nurses, and senior practitioners to  have an equal perspective when performing an initial patient evaluation.

The authors liken the process of automating primary care to the process of clinical trial inclusion and denote six steps for the development and application of the automated medical system. Here it is denoted with a determinant of how WellAI’s solution fits the criteria:

1. Do no harm:  Understanding the fit in the clinical workflow and where the impact will be is critical. The authors indicate that less complex patient conditions are better addressed by automated algorithms. WellAI is best fit on the front end of the clinical workflow where it provides initial triage to direct patient care.
2. Choice:  Patients should have the option to opt out. With WellAI patients who prefer using smartphones and having the convenience and opportunity to handle medical issues and communicate with the care team or physician have the ability to use the solution. Those who do not choose WellAI may use portals or the good ‘old fashioned’ phone call.
3. Disclosure:  Delineating the way the automated process works needs to be disclosed. How WellAI’s solution addresses medical conditions and the respective limitations of the solution are clearly delineated with the purpose of application.
4. Personalization: Patients should tell the algorithm their preferences for treatment. WellAI has not attained that level of sophistication as of today, however, it is – on the roadmap.  Discussion has been proffered with respect to integrating WellAI’s medical journal diagnostics with patient data present in the EMR/EHR – a significant advancement in AI driven healthcare.
5. Degrees of Automation:   A clinical process may be partially or fully automated. With WellAI, initial patient diagnostics and front office workflows are partially or fully automated based on the configuration desired for a particular office.
6. A Learning Healthcare System: Automated primary care will be a hallmark of learning and adapting healthcare systems. WellAI represents a small step – an easy step – for medical practices wishing to adopt more sophisticated and advanced technology. At some point, in the near future, practices that do not automate processes whether through AI/NLP or robotic process automation (RPA) or lesser sophisticated technologies – better practice management solutions or portals – will be left behind as patients themselves demand more from practitioners.

Automating primary care has obvious benefits in addition to driving better clinical execution and efficiencies like reducing anxiety, burnout, and workplace pressures. It also has less obvious benefits like positioning practitioners as sophisticated users and incorporators of technology for the good of patients. Adaptive algorithms that keep up with the literature and treatment are desired along with the support and input of medical practitioners. WellAI is one such solution that neatly fits the definition of automating primary care and represents the next generation of clinical automation.

Background.  Founded in 2020, WellAI, an AI health-tech company, is the developer of scientifically and technologically advanced medical applications. WellAI’s engineers, fresh off the development of a COVID-19 research tool (presented at the IFCC annual conference) leveraged their expertise into developing an advanced clinical diagnostic tool (triage solution)  for physicians, caregivers, and employees/individuals. The company is the developer of the Digital Health Triage Assistant, WellAI for Medical Providers, and the Adaptive AI Diagnostic Engine. It also provides custom solutions. The AI Diagnostic Engine has uniquely assimilated 30+ million medical studies and has the ability to diagnose, with 83%+ average accuracy, more than 500 health conditions including pediatric specific conditions using simple spoken language in less than 1 minute.

 

WellAI Team

wellai.health