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AI in Healthcare: Technology is here, users are not




Since the beginning of the year, there has been a significant increase across healthcare plans, providers and analytics companies to use AI to change the way they deliver and care for their patients.

Today, AI applications in healthcare are across the map. Data from my company’s digital health intelligence database, DamoIntelTM, confirmed a significant increase in AI use case launches across clinical and management areas in 2020. By analyzing AI / ML applications deployed by the top 50 health systems across the United States, AI-enabled solutions include machine learning, natural language processing (NLP), conversational interfaces such as chatbots, robotic process automation (RPA), and more. It falls into several technology categories. COVID-related use cases in the clinical and administrative fields have contributed to the increased adoption of new technologies such as chatbots in healthcare.

Focus on real-time intervention with AI-enabled solutions at Point of Care

The biggest challenge for AI-enabled care is to provide real-time insights into the Point of Care clinical workflow. For example, speech recognition technology is effective for low-level tasks such as recording doctor-patient encounters. However, they have not yet evolved into decision support systems that provide additional insights in points of care for diagnostic and treatment decisions.

On the other hand, solutions that can provide real-time insights are still large and have not been widely adopted. An example is Stanford University’s smartwatch-based COVID diagnostic app, which works with Amazon to analyze elevated heart rate and other abnormalities and send real-time alerts to patients suspected of having a COVID infection. Dr. Michael Snyder, a professor and chair of genetics, is working to extend the solution with the goal of creating a continuous monitoring framework for health indicators at the individual level. His goal is to cover anyone who has a smartwatch. Amazon has provided millions of cloud computing credits for similar diagnostic solutions for digital health innovators around the world.

Data collaboration to drive advanced real-time analytics

If there’s one new trend this year, it’s data collaborative. Truveta, a consortium of 14 healthcare systems launched in February, aims to pool patient data from all member systems to facilitate advanced analysis to improve healthcare outcomes. .. Google has announced a series of partnerships with healthcare companies such as Mayo Clinic, Ascension Health and Highmark. Use cases include, but are not limited to, quality measurement, benchmarking, and data analysis of management reports. In addition to its partnership with Google, Mayo Clinic has launched a new data collaboration initiative with AI startups targeting data from remote surveillance devices. Highmark, a leading Pennsylvania-based health insurance company, has a 10-year partnership with Christiana Care in Delaware to pool medical and billing data for better results. Expect more consortia to be seen as larger payers and providers pool datasets and increase efficiency through advanced analytical insights.

Other trends driving the future of AI-enabled healthcare According to CMS final rules that allow patients to access their medical information and share it with developers looking to build new digital health products and services. Innovations in AI-enabled applications are on the rise. A futuristic room that incorporates a great experience driven by AI-enabled digital interactions between caregivers, patients, and their families. One example is the $ 1.5 billion investment in pen medicine in Philadelphia. The 500-bed facility, entitled “Pavilion,” features a room with an interactive 75-foot monitor on the wall. John Donoue, Vice President of Entity Services for Medicine, has been deeply involved in the technology realization of future hospital rooms. He mentions Disney-inspired user experience design as part of a six-year project under construction. Analysis from a remote monitoring device. AI-enabled applications that capture and analyze large amounts of data from home surveillance devices and sensors will take the next step in the evolution of healthcare. As medical care moves from hospitals to home, significant investments are expected in analyzing data from remote sensors and surveillance devices. Amazon’s recently launched Amazon Care products include home care in addition to virtual care services as part of the entire package. Large-scale medical systems such as Kaiser Permanente and Mayo Clinic have also appeared in the game. They have announced an investment in Medically Home, a technology company that primarily provides home care. Is the patient ready now or is she?

Although AI-enabled care technology and computing infrastructure are mature, the adoption of AI-enabled care concerns the various levels of readiness of existing companies in the current healthcare ecosystem and the safety of particularly complex AI-enabled care. Driven by concern. Clinical condition.

Patients are also uncertain about AI-enabled care. Recent studies have pointed out that patients find chatbots annoying and hesitate to receive medical advice from bots. Management use cases for AI-enabled applications have the potential to achieve better ROI in the short term. Sachin Patel, CEO of Apixio, a healthcare analytics company acquired by Centene in 2020, is demonstrating four to seven times more revenue than AI applications in financial operations such as risk adjustment.

According to a survey by my company, more than half of all hospitals in the country continue to use electronic health record (EHR) systems as their primary point of care tool. New cloud-based AI-enabled solutions continue to face the challenge of seamlessly integrating into clinical workflows at Point of Care. Interoperability concerns and the challenges of standardizing and normalizing medical data continue to be important challenges for AI-enabled applications. In addition, standards such as ICD, SNOMD, and FHIR continue to evolve, demonstrating the ongoing demand for reliable code change management and data normalization solutions validated by subject matter experts. New data sources, such as genomic data, require additional ethical and privacy guardrails before they can be used in AI applications.

The final concern about AI in health is related to the lack of visibility into how algorithms are trained to work in health, exacerbated by the systematic bias inherent in many AI applications. .. Despite advances in AI technology, dataset-trained algorithms can be easily transferred to another dataset, especially as health operational data and social determinants play an increasingly important role in population health risk assessment. You can not. As cloud platforms become the leading data repositories for developing AI-enabled solutions, data privacy concerns drive the trust and consent needed to drive the adoption of AI tools.

The bright thing about AI in health care is the fast pace of adoption of AI in management functions. Healthcare system executives need to extend the reach of these applications to cover new operational areas such as access and patient involvement to drive improved efficiency and quality of experience. Clinical leaders need to continue to carefully expand the use of AI applications and focus on areas of operation that do not necessarily replace human intuition and judgment. An example of this is the use of AI to optimize chemotherapy schedules in pen medicine.

As health care leaders are accelerating the adoption of AI, the costs and benefits of the efforts involved in developing and deploying AI solutions need to be carefully weighed. The question is always what can be done with the insights gained from AI applications. If the needle cannot be moved with insight and information, clinical leaders need to question the value of the program and the energy needed to generate the insight in the first place. The important thing is to invest in and build from areas where you can see proven results. We are still a few years away from the proliferation of AI in the core clinical aspects of healthcare. Until then, I’ve been pushing the frontier.

Copyright © 2021 IDG Communications, Inc.

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