Combining AI-Led Analytical Thinking With Compassion



Rohan D’Souza is currently Chief Product Officer of Olive AI — leading the charge on building the Internet of Healthcare.

Even before the Covid-19 pandemic, burnout among healthcare workers was well documented. When the pandemic turned it into a crisis, surveys showed rising anxiety and depression among healthcare workers who are increasingly considering leaving the profession. That’s a scary thought for patients and for an industry already suffering from labor shortages.

Part of the problem is that people who have dedicated their lives to helping others are being asked to do more administrative work. A recent survey by my company found that 92% of clinicians agree that too much time spent on administrative tasks is a major contributor to burnout.

New health information technologies such as electronic health records (EHRs) were envisioned to support the best possible care for the patient by replacing cumbersome paper-based processes. Instead, EHRs have become an ongoing source of frustration because they have increased the administrative burden. Clinicians and other healthcare providers argue that the EHR has introduced new challenges, such as more documentation, unfavorable workflow changes and input errors.

Balancing Automation And Human Touch

Improving healthcare requires more than just applying the latest digital tool. By not starting with empathy and by not applying human design thinking into today’s widely used software products, humans work for software rather than software working for humans. To solve this challenge, we need to develop technologies with front-line workers rather than for them.

Healthcare executives recognize the opportunity for automation and other artificial intelligence-enabled advancements to reduce administrative burden. For example, AI can analyze massive amounts of unstructured data and identify patterns. It increasingly can be applied to more complex use cases because it mimics human logic.

All jobs are vulnerable to some degree to automation. But healthcare is built on compassion, a human trait AI can’t replicate. We can’t teach a robot to love and show empathy and compassion, but we can teach it to update the EHR while a physician examines a patient to alleviate the tiresome task of data entry.

Healthcare executives must balance optimization and the human touch when it comes to automation. It’s a tricky challenge, as our research cited found that executives are uncertain about which processes to automate to provide the greatest return on investment without affecting the quality of care.

Plotting AI Strategy

To help, we can look to well-known computer scientist Kai-Fu Lee. He has a blueprint for how humans can coexist with AI that sheds a lot of light on how healthcare can intelligently integrate automation to ensure the stability and resiliency of our system.

Lee views AI adoption in a matrix instead of a spectrum. For his blueprint, he creates a two-by-two grid with optimization and creativity along the X-axis and compassion on the Y-axis. We can group healthcare activities into each quadrant.

In the bottom left corner, what I would call the furthest from the bedside, are the numerous, complicated administrative tasks that support healthcare today. AI can be used in various areas that can provide substantial efficiencies, including claims processing, revenue cycle management, clinical documentation and medical records management. This area demands ruthless optimization as administrative costs continue to spiral upward and take away resources from areas that matter most.

Moving to the right are more complex strategic activities that also have a role for AI. For example, denials management has become a vital financial function inside health systems as we shift from volume to value. AI can help streamline denials management by correcting errors and resubmitting claims. It can also augment the human team by sending alerts and insights into common problems people can address on the front end.

Moving up, however, we start getting into clinical workflows that require AI-enhanced data and analytics wrapped in the warmth and compassion that only humans can provide. Medicare payment reform and transitions to other risk-adjusted payments put more pressure on physician practices to improve documentation and capture the coded data necessary for accurate payments. Risk adjustment is also about chronic disease management, which requires clinical knowledge to identify chronic illnesses and address care gaps.

In short, as healthcare considers its future and our aging population, we will need more compassionate caregivers to give more medical care to more people. Transforming the nature of work in healthcare is imperative for everyone involved.


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