The impacts of AI in healthcare in the decade ahead

As the healthcare ecosystem continues to transition from volume-based care to value-based care, we will see significant investments and related advancements in applications of machine learning – which is the underpinning of artificial intelligence of computers/processes used in healthcare.

From machine learning, we are seeing the explosion of another common buzz phrase natural language processing (NLP) which is an application of the computer learning "on its own" through the use of neural networks to build predictive algorithms based on a feedback loop of learned understandings - which continues to get smarter with the more experiential data ingested and analyzed.

Whether it's a hospital, health plan, medical group, pharmaceutical company or medical device manufacturer, the focus over the next decade and more will be on patient-specific innovative technologies and data-driven solutions that improve the quality of care while simultaneously reducing costs. Machine-learning technologies will play an ever-important role in this, ensuring that the healthcare system as a whole is continually "learning" and applying those insights to real-time decision-making – for individual patients – at the point of care.

Today's machine learning platforms expand upon the classic regression techniques which are found in traditional predictive analytic engines in order to derive a more dynamic, patient-specific understanding of health care systems and most importantly, individuals. For data scientists, however, the Holy Grail is doing all this in real-time in a broad-spectrum manner leveraging disparate, big data – another buzz phrase of recent. The ability for a computer system to analyze thousands of disparate and evolving data points to create patient-level predictions driving more precise treatments is a game-change for many clinical conditions.

We're already seeing real-world applications of machine learning that promises to drive greater efficiencies in healthcare. With the application of Natural Language Processing, managed care organizations and health plans can benefit by conceivably no longer needing to exhaust often scarce and costly resources to manually read and analyze millions of patient medical records for data-submission audits by CMS and other regulatory authorities. Applications of NLP – powered by machine learning – can now be used to pre-screen medical records with a greater efficiency and completeness over the traditional approach of human clinical review of often lengthy medical records delivering bottom-line savings in the process.

AI will change the healthcare system, affecting physicians and hospitals

The U.S. healthcare system has been plagued by rising costs and inefficiencies attributed to the fragmented nature of care delivery and communication. As frameworks for interoperability and interconnectedness evolve to eliminate healthcare siloes, there will be opportunities to more quickly aggregate historically fragmented disparate data sources – which, when applying advanced parallel processing, the ability for real-time machine learning and NLP and other AI-related approaches can be realized. The goal is to make that enhanced, patient-specific analytical derivative available to the healthcare delivery system so it can change how payers, providers, and other healthcare organizations engage with patients and drive better outcomes under value-based care.

As such, scalable big data platforms can empower the future of machine learning, impacting health systems and clinicians significantly as they make the transition to providers that are decision-makers guided by real-time patient specific analytics at the point of care. The application of these expanded data and analytic models derived in part through machine learning technologies will also drive improvements and efficiencies elsewhere across the healthcare ecosystem, among other things helping reduce medical errors while improving cancer detection and diagnosis of mental health conditions.

But what's coming is even more interesting. Take for example telemedicine; it is now very much conceivable that in the not too distant future, clinicians will be able to access in real-time longitudinal patient clinical data during virtual visits including clinical recommendations guided by an individual's unique genotype, phenotype and socioeconomic circumstances. This is a further extension to what many are speaking of with precision medicine – yet another buzz phrase growing in relevance in recent years.

Physicians and hospitals should prepare for AI in the future

Methodologies under the AI umbrella, such as machine learning and NLP are slowly becoming part of the care delivery continuum, and a growing number of stakeholders across the healthcare ecosystem are making investments with these technologies which rely heavily on an interoperable system that can integrate and aggregate disparate complex data inputs. This is true within pharmaceutical discovery and commercialization where data is changing the traditional paradigms for clinical trials and drugs are now accompanied by companion diagnostics that ensure the right drug reaches the right patient at the right time.

The healthcare delivery ecosystem is preparing for machine learning and other AI-related technologies by increasing its investment in scalable, modular platforms that deliver critical patient-level insights directly into clinical workflows in real time. Hospital systems, integrated delivery networks, accountable care organizations, independent physician associations, and other physician networks now realize that they must embrace technology platforms that enable large-scale data integration and aggregation which fuel analytical competencies that rely heavily on mastery of large comparative datasets.

Ultimately, clinicians want to – and should – spend more time with patients. Relevant and focused data-driven insights can enable this, but there is a steep learning curve for some. Perhaps it's a stretch goal, but wouldn't it be nice that by the end of the next decade, nearly every 'data point' related to a patient - whether that be from their genotypic & phenotypic profile, their self-reported commentary or a wearable device, or even insight into what the patient purchased at the convenience store the night before - were aggregated and analyzable to inform and thus improve the patient-specific quality of care delivered. When this happens, we will undoubtedly be far along in the transition from volume-based to value-based care, and machine learning, AI-related technologies will be a big reason for this.


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