In the last 50 years, there have been significant developments that have forever improved the delivery of healthcare. Consider imaging (MRI/CT scans), minimally invasive surgery, anti-viral therapies, precision medicine (e.g., CRISPR and gene editing) and near or complete eradication of diseases like polio and smallpox. As we look back 50 years from now, we will have experienced seismic changes from AI. When I’ve spoken with healthcare leaders, they’ve pointed to a progression of AI adoption that will occur in the following three categories, which you should consider for your organization: administrative, research and development, and care delivery.
Area 1: Administrative
Administrative spending accounts for 15-30% of total healthcare spending in the U.S., much of which is considered wasteful. Furthermore, administrative tasks drive provider burnout: 2016 data showed that physicians spent 48% of their time on electronic health records (EHR) and desk work compared with 26% on direct clinical face time with patients.
AI is having an impact on clinical notes and medical coding. Microsoft, through Nuance, developed an AI-powered solution that documents patient encounters at the point of care. Abridge also uses AI to generate clinical notes from patient conversations. In your own organization, consider the hours that could be shifted from documenting care to care delivery.
Prior authorization (PA) for medical services is another area ripe for AI disruption. PA is estimated to cost $25 billion yearly, and 93% of physicians have indicated that PA “can delay access to medically necessary care.” One health plan operator that received more than 1.5 million prior authorization requests in 2022 implemented an augmented intelligence solution that allowed it to process requests up to 1,400 times faster than in the past. However, there have been challenges to AI denials of PA requests. In response, the House of Delegates directed the AMA to advocate for greater oversight over this process, including whether insurers are requiring a human examination of patient records before a denial.
This is an important point: AI has the potential to reduce time and costs, but successfully rolling it out requires building trust with patients and providers. Consider where human engagement is needed when rolling out such solutions.
Area 2: Research And Development
Jensen Huang, president of NVIDIA, claims that “‘digital biology’ is going to be the ‘next amazing revolution’ for AI.” It is no exaggeration to say that AI can change the way therapeutics are discovered and developed, with the potential to improve speed, reduce costs and improve access to treatments.
At the stage of target identification, AI could mine -omics data, analyze phenotypic results and identify novel binding sites, among other activities. It could then be used to predict pharmacokinetic and pharmacodynamic properties. Companies are pouring funding into the space; recently Xaira, a company using “machine learning, data generation and therapeutic product development to build a platform for drug discovery,” emerged from stealth with $1 billion in funding.
AI is also speeding up clinical trials. Up to one-third of the time for clinical trials is spent recruiting patients, and one in five trials do not recruit the required number of people, according to a Nature article. AI can help in designing trial criteria, identifying suitable cohorts for trials and alerting medical staff and patients about trial opportunities. For example, H1 offers an AI-enabled healthcare data platform that, according to its website, aids in R&D pipeline acceleration and trial design. AI can also be used to facilitate diversity within clinical trials; Acclinate uses its analytic platform to identify locations for trials, reach hard-to-find groups and mobilize the recruitment process.
Sixty-eight percent of pharmaceutical businesses “identified pervasive AI as the technological trend with the highest potential impact on their business/market environment.” Ask your leadership team how AI can glean new insights from your clinical data or how to partner with clinical research organizations or life sciences companies to bring new products to market.
Area 3: Care Delivery
Within care delivery, I am most excited about the potential of AI to allow providers to uplevel their skills. While your doctor will not be replaced by AI, doctors who use AI may replace those who do not. Clinical adoption of these innovations must first prove efficacy and then fit neatly within the provider’s workflow.
In the near term, AI has the potential to augment how hospitals use data, 97% of which goes unused (registration required). AI-powered diagnostics and predictive analytics can take advantage of this data. Aidoc has developed cardiovascular solutions that consolidate data, generate clinical insights and streamline follow-up conversations with patients. AI can also help interpret medical images, helping patients see results faster and identifying concerns. In breast cancer detection, AI can enable radiologists to better view anatomical structures. It can also enable remote interpretation of imaging, which can help drive equitable access in low-resourced areas.
Care delivery will require a significant human footprint in the near- to medium-term. Leaders should focus on finding ways to leverage AI to provide high-quality, relevant information to providers in a timely manner. As providers become more comfortable using AI on a day-to-day basis, I believe there will be greater opportunities for future use cases.
My three recommendations for leaders are:
1. Balance Impact And Risk: Understand your organization’s risk appetite. Evaluate the potential risks of using AI in any given situation (e.g., bias and lack of accuracy). Think through where a solution may require a “human in the loop.”
2. Build Or Partner For Capabilities: Evaluate where your organization may need its own custom tools and approaches versus where you can partner to leverage existing models.
3. Change Management: Adapting to a new way of working can be challenging. Incorporate end users in your design process and allow for time to increase their overall comfort level with any solution.
AI has the potential to be a positive, transformative force within healthcare—it is up to leaders to ensure we are prepared to harness its power.
——————————————————
Originally Published On: Forbes
Photo courtesy of: Getty
Follow Medical Coding Pro on Twitter: www.Twitter.com/CodingPro1
Like Us On Facebook: www.Facebook.com/MedicalCodingPro
