The Role of AI in Healthcare Management & Administration

In Q1 this year, business leaders were already proclaiming 2022 would be a banner year for AI-driven business growth in all sectors willing to commit and invest. Nevertheless, delivery on that promise has –and still – varies widely by industry, leaving the uninitiated with the challenge of finding real substantive developments in a whirl of venture capital buzz and hype. Among industry transitions ripe for digital transformation, applications of AI in healthcare stand out as a case for potentially sweeping innovations from the boardroom to the point of care. 

Charting emerging industry changes by trends in venture capital speculation, more than 40 AI healthcare startups have raised at least $20 million for new product developments in just the last two years. The global AI in healthcare market had an estimated value of $8.23 billion in Q4 of 2020. Current projections put the market on pace to cap $194 billion by 2030, exhibiting a sustained compound annual growth rate (CAGR) of 38.1% for the decade. 

For forward-looking healthcare organizations looking to leverage the cutting edge of technology for better patient outcomes and more efficient operations, understanding the role of AI in applications for patient care and strategic data management has become a critical hurdle to cross. In this guide, you’ll learn what current AI technologies offer and how healthcare management and administration professionals are applying those technologies to drive better patient care. 

Key Takeaways:

• Artificial intelligence (AI) is a booming technology, attracting significant investment and developing by leaps and bounds every year.
• In the face of rising costs and ongoing labor shortages, the healthcare industry benefits broadly from new AI applications for better patient care and more efficient resource management.
• Recent developments in AI data modeling show significant promise in helping organizations deliver better quality care to more patients, even in the context of potential resource limitations. 

What Is AI?

Artificial intelligence (AI) is the simulation of human intelligence in machine data processing and computer systems. For non-specialists in computer science, the term AI conjures an undefined overlay of notions drawn partially from science and partially from science fiction. Getting a handle on what AI does – and does not– helps to draw a distinction between AI design and function.

• AI Design: In computer science, AI programs mimic decision-making processes in human minds. They are adaptive and exhibit varying degrees of unpredictable autonomy based on the data they consume.
• AI Functions: In practical applications, AIs offload labor from human workers to machines. In many cases, AI labor is superior in speed and overall functionality, processing qualitatively large data sets in fractions of the time human labor would require. 

Image Source: https://www.ibm.com/cloud/learn/what-is-artificial-intelligence

In all applications, AI is definitively dynamic and oriented towards problem-solving. The capacity to emulate human problem-solving processes breaks down into a range of subfields in programming, such as machine learning and, within that, deep learning. Learning enables AI to be predictive, assuming and enhancing otherwise human responsibilities. 

3 Recent AI Applications in Patient Data Modeling

Ultimately, it will fall to legislators to define the role AI should have in industries such as healthcare with unique ethical concerns. However, AIs have already begun enabling breakthroughs in patient care through enhanced data modeling and master data management. Here are three recent examples. 

1. Machine Learning Analysis of Heart Sounds to Diagnose Congenital Heart Disease (CHD)

A 2021 study of different machine learning programs for interpreting stethoscope heart sounds and diagnosing CHD found that programs based in neural networking demonstrated a capacity to correctly diagnose a range of CHD conditions. With additional development over time, such programs would increase early CHD diagnoses and expand the diagnostic abilities of healthcare systems with limited resources. For healthcare administrators, adopting AI solutions in diagnoses can deliver better patient care through early detection while simultaneously alleviating the burden of maintaining trained personnel capable of performing complicated diagnoses at every point of contact. 

2. Predictive Data Modeling for Susceptibility to Substance Abuse in Patients Prescribed Opioids

Pain management is often one of the more challenging problems organizations face in providing the right level of care while preserving the Hippocratic imperative to do no harm. As painkiller classes such as opioids are indispensable to effective pain management and highly addictive, AI-based data-driven prescription protocols offer organizations a better chance of managing pain without precipitating substance abuse issues.

This study conducted by the Courant Institute of Mathematical Sciences at the University of New York applied AI-based predictive modeling to patient histories to identify patients with high risks for opioid abuse and mortality. Compared to the 76.6% accuracy of traditional regression analysis, the AI-enhanced model demonstrated 94.3% accuracy in identifying patients who should only receive opioid prescriptions in the context of supplemental monitoring. 

3. Cloud-Based Data Lakes for Long-Term Follow-Up Data

Certain types of cancer, though common, remain difficult to predict in severity. Prostate cancer is the most commonly diagnosed cancer in men all over the globe. Nevertheless, diagnosing care providers struggle to predict at diagnosis which patients will experience decreased lifespans and diminished quality of life. 

As a part of long-term patient care planning, AI has the potential to deliver more predictive prognoses through exhaustive correlation with outcomes in past cases. To this end, the ReIMAGINE Program is currently gathering prostate cancer data from men at screening, de-identifying patients from their data, and moving all clinical information with subsequent follow-ups and outcomes to a multimodal cloud-based data lake with an application interface for AI predictive analytical tools. The growth in data over time should allow diagnosing physicians to make better treatment decisions as soon as prostate cancer is detected. 

Healthcare Master Data Management with Coperor by Gaine

To meet the evolving challenges of effective healthcare data management, Gaine has developed an industry-first comprehensive master data management platform designed by healthcare professionals for healthcare professionals. Scalable and capable of integrating data across your organization and contracted partners, Coperor offers healthcare organizations the shortest past to a single source of truth in managing data scattered across uncoordinated systems.

To schedule a live demo of the Coperor platform, contact Gaine today.