Artificial intelligence in healthcare is driving innovation in data analytics

Artificial intelligence (AI) applied to healthcare wasn’t on the radar 10 years ago. Outside of academic circles, relatively few people were even aware of the potential of the technology. It seemed like a distant fantasy. Today, though, that’s all changed. AI is the new trend in innovation, and everyone in the sector is talking about it.

Practitioners are cautious, though. Unlike in the regular software field, the mantra “move fast and break things” doesn’t work well in the healthcare context. Patients’ lives are on the line, and clinical establishments must always temper their technological aspirations with a heavy dose of caution. With that said, the applications of the technology are truly astounding, even today. AI offers the medical industry a chance to add “minds” at near-zero marginal cost, reducing cognitive burdens across the industry. It could open up new efficiencies and, potentially, make processes more accurate and consistent, ultimately helping patients.

Application of AI and machine learning to complex clinical data sets

Patient data is complex. Unlike most panels, data points are interrelated and regularly changing. For instance, a patient’s return depends on the nature of their condition and the quality of their treatment.

Patient datasets can also be noisy. When multiple medical practitioners interact with patients, they may add to the total stock of data, producing duplications and inconsistencies.

It can sometimes be challenging to access data or drive change. Again, AI could help tremendously in these areas.

Data anomaly detection

Data anomaly detection is a form of data mining that evaluates data entries and searches for anything out of the ordinary. Traditionally, analysts used it to look for technical glitches or changes in consumer behavior. But it has essential applications in the healthcare setting, too, allowing medical professionals to monitor data placement and ensure quality. Physicians, for instance, could use AI techniques to highlight issues in treatment progression, facilitating a higher quality of care.

On the clinical variation front, clinical data anomaly detection helps to detect different treatment patterns and alert management. AI looks for similarities in underlying patient characteristics and then flags variations from standard protocols, allowing medical practitioners to investigate the reasons for the change.

Related/unrelated returns

Healthcare AI and predictive machine learning models are also helping in other ways. For instance, smart software can now determine which patient returns are related to the primary surgery and which aren’t. For payers, this technology is important. It means that they can quickly determine which medical procedures relate to the initial treatment and which don’t. Thus, it can help them better manage their costs and determine what is and isn’t covered under a bundled payment or episode of care.

AI can also help identify claims unrelated to the primary surgery, refining the data and providing third parties with a more accurate and granular view of patient episodes. Flagging unrelated claims within payer processes automatically could increase efficiency, cutting the overall burden of healthcare on national resources.

Procedure ranking

Most clinical settings record procedures according to the time that they occurred. AI, on the other hand, allows for new modes of analysis by enabling ranking by importance instead.

Procedure ranking by cohort family helps identify episodes of clinical variation and missed opportunities to lower costs and standardize patient care. Practitioners can use these tools to look for differences in patient outcomes, such as inpatient length of stay when using different types of medication. Ranking by alternative metrics may indicate methods for reducing operating room time and reducing costs by not performing common secondary procedures.

Conclusion

The list of AI applications is potentially endless. Data is valuable. But underlying everything is the ability to structure data for meaningful analysis. AI helps arrange information in a way that clinical practices can use to bolster their operations, lower costs, and speed patient transition and recovery. Opportunities for improvement abound, from reducing the cost of surgery to optimizing the outpatient experience.

Combining procedure importance with the total cost of care also offers critical economic advantages to clinics recovering from COVID-19 and looking to improve revenue generation in the coming months. Clinics can better identify problems in their processes and find the most effective procedures to support their cash flow. AI offers healthcare providers a host of advantagestoday. The number of potential applications is ultimately boundless, and current capabilities are improving all the time. Machine learning and other cognitive systems can now automatically detect new clinically relevant data groupings, scientifically identify instances of clinical variation and review best practices. Data no longer needs to be a “black box.” With the right tools, it is highly amenable to rigorous investigation.



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