BY SANDRA ZAWALSKI, MSN, RN, CRRN, CCM, ABDA, MSCC
In recent years, artificial intelligence (AI) technology has made significant inroads in healthcare, and its impact on case management has been particularly notable. Case management is a critical aspect of healthcare, with the goal of being a patient advocate and ensuring that patients receive the most appropriate care in a cost-effective manner. AI has shown early success in streamlining and optimizing the case management process, but there are also precautions to consider when using it. This article will explore the benefits and safeguards for consideration as they relate to the use of AI in the case management role.
There are plenty of benefits of using artificial intelligence. AI can assist with appointment scheduling, using algorithms to prioritize caseloads and complete many of the administrative tasks that most case managers perform now. This could lead to better use of the case managers’ time and allow them to better advocate for their patients.
Cost savings could be identified faster and more efficiently. What actions did the case manager take during discharge planning to decrease a chance of a 30-day readmission? Did those actions save the patient and/or hospital or insurance company an unnecessary cost? Did the case manager negotiate on behalf of the patient for a decreased cost in durable medical equipment or other medical supplies?
Improved patient outcomes. AI can analyze those patients who were high risk for long lengths of stay using algorithms. This can assist the case manager in putting those patients in their high-risk category and understand these patients may need more time than one who is not at high risk. When looking at patterns in a patient’s medical history and current health data, potential health risks can be predicted. This can allow healthcare providers to be proactive with preventative care that could lead to improved outcomes and reduced costs.
Of course, with benefits, there is also risk, including some the case manager needs to be aware of and take precautions against. The first concern is that algorithms may be susceptible to bias and could lead to unfair or discriminatory treatment of certain patients. The human touch can go a long way in these cases. So, should AI be used only? The answer would be no. The best way for an optimal outcome is to coordinate AI with the human touch. In other words, don’t take the information AI presents for granted.
You may also question the quality of the data. Bad data in equals bad data out. If the algorithms are not correct, the data will be incorrect, skewed, or not what the end user is looking for. This is why the case management department needs to work closely with their IT department. Prior to developing any algorithms, the decision needs to be made on what you want to be reported.
One also has to look at any integration they have. Not all systems are compatible with AI technology. Many systems that are in use are a few years old, and despite AI being around for some time, it has really come to the forefront during the past few years. And, the more AI is used, the smarter and quicker it becomes.
And, of course, there is always that chance for data breaches. This has become more common and will probably increase as the use of technology increases.
So, how is AI currently being used for case management? It is used when looking at social determinants of health. Tracking patient insecurities can assist the case manager with discharge planning. No transportation? The case manager can research public transportation, taxis, buses, etc. Looking at possible readmissions, taking data and predicting specific diagnoses that may have an increased opportunity for readmission.
Decision making. There are companies that are involved with programs for utilization review to assist the UR nurse in determining if a patient should be in an observation versus inpatient status.
AI is able to analyze millions of cases and can triage denials to determine why a claim was denied and to assist in determining which denials have the highest probability for a successful appeal.
The bottom line is that AI has quickly become the wave of the future, and it doesn’t seem to be slowing down. One of the frequently asked questions I hear is if AI will replace case managers. Patients still need that human touch. It has been researched and documented that touch is fundamental to human communication, bonding and health. I like to think AI will augment what case managers currently do, taking the mundane tasks and allowing case managers to focus more on people.
Despite some of the current limitations, today’s AI has the potential to significantly improve the efficiency and effectiveness of case management. Rather than approaching the technology from the dual perspectives of fear or overconfidence, a balanced approach with thoughtful inspection and design can produce all types of benefits in the healthcare industry from cost savings, to staffing efficiency and ultimately, better patient outcomes.
Sandra Zawalski, MSN, RN, CRRN, CCM, ABDA, MSCC, is a registered nurse with almost 40 years of experience in a variety of clinical settings that include orthopedics, brain injury rehab and neonatal intensive care. There is extensive experience in case management for payers and providers holding leadership positions in each, as well as owning her own consulting business. She has a master’s in nursing with a focus on education, is a board-certified case manager, certified rehabilitation nurse, certified as a Medicare Set Aside Consultant, a member of the American Board of Disability Analysts and a designated ATD Master Trainer. Sandra has published numerous articles in case management professional journals, is a regular contributor to CMSA Today and was a regular contributor for Just Begin magazine. Sandra is a former commissioner for CCMC, a former chair and has had the privilege of speaking at national conferences including CMSA and CCMC’s New World Symposium as well as a certified Cert 360 facilitator for CCMC. Sandra’s current position is as a principal clinical educator with MCG.
REFERENCES
https://www.foreseemed.com/artificial-intelligence-in-healthcare
https://www.jstor.org/stable/j.ctv5vdcvd.5?seq=1
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