Highlights:
Emerging digital technologies have been slow to enter the healthcare industry but have the potential to bring about significant change over the next few decades. The term “Artificial Intelligence” (AI) is used broadly in almost every sector today to define anything that can produce results with minimal human interference. There is consensus among healthcare leaders that AI will be the most disruptive technology in the space. Many AI-based products and services are emerging, but it is hard to understand for whom and where value can be created. This is because processes within the healthcare system have dozens of stakeholders; and the recipient of newly created value is not usually the payor for that new product or service. Hence, this article evaluates the various business models used by current health tech startups utilizing AI, to then categorize, clarify, and develop a framework that can be applied to new opportunities and use cases in emerging businesses as well as in existing businesses and clinical settings.
Major Findings
It is imperative that companies pursue a clear path regarding their AI strategy to ensure the survival of their business. The best way to do this is through true value creation for both patients (end users) and providers. Opportunity areas within healthcare that can observe the greatest disruption from the implementation of Artificial Intelligence can be broken down into “archetypes” or categories targeting specific processes and datasets. Successful business models can be built around these focus areas. Most of these focus areas involve connecting disjointed data among electronic health records, patient IoT device data, imaging, labs, and testing data, and data from other health sources.
There are infinite opportunities to create value from this data. Examples include:
Providing patients and providers with holistic insights on the status of a person’s health.
Providing preventative advice/ insights directly to patients as the software analyzes their health data and habits in real time as measured by their IoT devices (smart watches etc.).
Showing patients potential precursors of a chronic disease state based on their real time health data and offer to connect them with potential providers who specialize in that disease state, skipping the need for a precursor primary care visit and potentially lower costs overall.
Acting as an assistant to primary care providers wherein a provider can input a patient’s current ailments and receive recommendations on differential diagnoses with data backing up each one.
Empirical Basis for Findings
Currently, various disjointed data from the patient side and the provider side can be combined to create new products or services that provide a holistic picture of a disease state, a person’s health, or help in diagnosing by linking various data sets together. These opportunities are not limited to hospitals and clinics, but are also available for other areas of healthcare including for pharmaceutical companies, pharmacists, therapists, and administrative staff. However, the problem today is that the healthcare industry is far behind in terms of digitization, privacy, and controls on data flow. Patients are not in control of their own health or health data, they are just “objects of a value chain system.” The use of artificial intelligence to connect datasets and generate usable insights will enable patient empowerment, allowing patients to take control of their health – placing focus on prevention instead of reaction. This could also play a role in controlling rising healthcare costs across the board. Organizations that can navigate and innovate within and outside the system will be in the best position to successfully implement digital transformation strategies within the healthcare space.
Implications for Practice Management
With artificial intelligence, the number of potential business ideas and entrepreneurial opportunities for improvement in healthcare has exponentially increased. It is essential for new and existing organizations to adopt a specific path to solve a specific problem using AI: provider-focused or patient-focused. The healthcare value chain is complex. Understanding where along the chain a new technology can generate the most value is essential to allow for greater adoption and to have prospective customers embrace the benefits of AI. Targeting “low-hanging fruit” such as improving administrative or operational efficiencies with AI or the healthcare ‘archetypes’ as defined within this article will provide the best opportunity to successfully navigate and implement digital technologies in the healthcare industry. Moreover, AI improvements (through the low hanging fruit) or breakthroughs (through the healthcare archetypes) can be used as case studies for the potential digital transformation have to improve patient outcomes. Doing so will help accelerate acceptance and adoption of digital technology in an industry slow to embrace change.
Bibliography
Garbuio, Massimo, and Nidthida Lin. “Artificial Intelligence as a Growth Engine for Health Care Startups: Emerging Business Models.” California Management Review. 61.2 (2019): 59–83.
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