Lars leads Issio’s FedRAMP certification and compliance team. With a love for languages and degrees in both STEM and Humanities fields, he translates effectively between all levels of the organization to ensure a sound security posture. As a former teacher and returned Peace Corps volunteer, he brings a passion for public service to Issio’s mission to improve healthcare for both providers and patients.
The use of AI to improve efficiency and decision-making capacity in administrative, operations, and labor management functions has been well documented in various industries, particularly among cutting-edge technology companies. Amazon’s ability to proactively ship products in the direction of customers whom its AI predicts will likely buy those products is well known. IBM claims that through extensive implementation of AI in its HR functions, it saved more than $107 million in 2017 alone, in addition to numerous qualitative improvements.
The challenges that leading tech companies have begun to address through AI and machine learning tools are similar to those in the healthcare realm. A recent article in the Journal of the American Medical Association notes that administrative expenses make up as much as a quarter of all national healthcare expenditures, amounting to almost $1 trillion. (1) The American Hospital Association (AHA) says that labor-associated costs often make up 50% or more of hospitals’ operating expenses.(2) Yet in spite of well-publicized AI success stories from other industries, the adoption of AI in healthcare has been comparatively slow.(3)Studies have indicated that global AI spending in healthcare reached $7-8 billion in 2021, and they estimate that it will increase at a rate of 40-50% CAGR. (4)
The key vectors for the introduction of AI into healthcare are through diagnostic and treatment innovations, patient engagement tools, and administrative and operational management solutions. Much of that AI spending has been dedicated to the former two categories. In diagnostic imaging analysis, for example, radiologists use AI to aid in tumor detection and classification. Particularly in pediatric radiology, AI has helped increase the accuracy of tumor detection while decreasing time to diagnosis. (5) In patient engagement, tools such as the WHO’s virtual AI health worker facilitate access to personalized medical, mental health, and lifestyle advice and resources.(6)
In contrast, examples of AI in healthcare operations and labor management are relatively few in number. Even so, the tip of the iceberg in terms of the potential benefits of AI and machine learning tools is visible in a couple of cases. An AHA case study reports how an AI tool designed to automate employee license renewal tracking saved an entire healthcare system more than 2,800 HR labor hours in the first year of implementation. (7) Other experiments are still in their infancy: natural language processing AI is beginning to help physicians take notes more quickly. Some hospital command centers have begun using predictive analytics for bed management and patient flow. For others, AI solutions help determine individual nurses’ workloads according to the specific patient diagnoses. Many more challenges are still waiting for their own AI solutions to be built, from improving surgery center or utilization to predictive patient influx modeling according to external events such as weather and epidemic disease. The AI healthcare transformation is just beginning and has the potential to simultaneously save billions in administrative overhead and improve patient care.
Data will drive the 21st century. With the proliferation of electronic health records and the incorporation of other digitized healthcare management and administrative tools into day-to-day processes, huge quantities of data are being captured. At the same time, the COVID-19 pandemic has highlighted the weaknesses of the modern healthcare system, which is prone to staff burnout and resource waste. The next evolution in healthcare operations and administration will be the use of AI and machine learning to synthesize those data into actionable insights that guide decision-making, increase efficiency, and improve the healthcare environment for patients and providers alike.
4 https://www.reportlinker.com/p04897122/Artificial-Intelligence-in-Healthcare-Market-by-Offering-Technolo gy-Application-End-User-Industry-and-Geography-Global-Forecast-to.html https://www.acumenresearchandconsulting.com/artificial-intelligence-market
6 https://www.who.int/news/item/04-10-2022-who-and-partners-launch-world-s-most-extensive-freely-acce ssible-ai-health-worker
7 https://www.aha.org/system/files/media/file/2020/02/workforce-case-study-artificial-intelligence-automatin g-processes-for-professionals-2-2020.pdf
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