Posted on June 03, 2024 | 4 min read

Three Ways Provider Organizations Can Use AI Right Now


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Using AI in Healthcare

AI has unlimited potential in healthcare. The industry could be disrupted by new technology that, according to Deloitte, is capable of “everything from answering the phone to medical record review, population health trending and analytics, therapeutic drug and device design, reading radiology images, making clinical diagnoses and treatment plans, and even talking with patients.”

As promising as the future is for healthcare AI, we’re not there yet. The technology still needs to mature and learn before it can be safely applied without any potential risk to patient safety. Along with those ethical considerations, provider organizations also need to feel adequately confident that AI can help optimize their operational or financial performance in the areas mentioned above.

Although we’re not ready to start scheduling patient visits with AI clinicians, there are still plenty of valuable applications for AI, machine learning (ML), and natural language processing (NLP) that provider organizations can take advantage of right now. We’ve spoken to leaders at health systems and integrated delivery networks (IDNs) at healthcare conferences like RISE, NAACOS, and ACDIS. These leaders shared valuable insights into how they have practically applied AI solutions at their organizations, and a few use cases in particular stand out. Read on to learn the top three ways provider organizations are applying AI technology.

  1. Improving population health
    A core tenet of value-based care is that all data—including data outside of the clinical record—should be aggregated and evaluated holistically. Social determinants of health (SDoH) and barriers to care have emerged as key data points that play an essential role in the predictive analysis that is central to population health.

    But the volume of medical data has exploded, and finding and parsing that data is no longer possible manually. With proper training, NLP can be used to comb structured and unstructured chart data to uncover information pertaining to SDoH and barriers to care, so predictive models are better able to stratify risk and inform patient care.

    AI is ideally suited for data analysis and pattern recognition, which is why many provider organizations are already employing AI in population health and chronic condition management. NLP helps identify patients with known chronic conditions or whose medical histories suggest they are likely to develop a chronic condition. Using AI to proactively flag high-risk patients allows providers to intervene earlier and, in turn, help their patients achieve better long-term health outcomes.

    To ensure prediction accuracy, the AI model should be trained only on data that is specific and relevant to the target population. When developed and deployed appropriately, AI can help providers make better, more timely decisions and support population risk stratification. This allows care coordinators to intervene at the right time to reduce readmissions and deliver more personalized care to patients who are most in need.

    Monitoring and adjusting to meet changing population demographics and needs is an immense task—and AI is ideally suited to handle the heavy lifting.

  2. Addressing clinician burnout
    According to the American Medical Association (AMA)’s 2022 National Report, 51% of providers are experiencing burnout, and many of them have considered leaving their profession.

    A key contributor to provider burnout is growing administrative burdens: clinicians spend anywhere from 12 to 16 hours each week outside of work hours completing administrative tasks, particularly documentation. To help reduce this workload, many clinicians are turning to AI-powered ambient voice transcription services that convert patient conversations to text for more complete clinical documentation in a fraction of the time. This allows clinicians to focus more on their patients, and it also has a financial upside: some providers have reported fewer encounter amendments since adopting AI transcription.

    Care teams are also using AI technology to improve operational efficiency after the encounter, using NLP tools to confirm that all comorbidities are identified and accurately documented. These tools can more quickly pull in key evidence from a wider array of sources—pharmacy utilization, claims, lab work, clinician notes, medical charts, etc.—to support suggestions for chronic or risk-adjustable conditions. These gaps are then presented for review, which dramatically reduces manual workloads for CDI teams.

    In the same vein, providers are also using AI to flag documentation that does not satisfy MEAT requirements and to help identify trends in documentation gaps for more effective and focused provider education. And by using AI to flag open recapture opportunities and build prioritized worklists, providers have been able to improve risk recapture and more accurately target their patient engagement initiatives.

  3. More efficient pre-visit planning
    In the days before AI, CDI teams had to manually comb through chart data and compile a list of suspected diagnoses ahead of the patient visit. By using NLP to do the prep work, CDI teams save themselves a significant amount of time and effort.

    Without the added burden of reading through stacks of chart notes for each patient, CDI teams can focus more fully on reviewing and confirming or removing the AI-derived suggestions. That allows clinicians to walk into each visit with a comprehensive and accurate list of suspected diagnosis gaps to evaluate while with their patient. for evaluation while with the patient.

    Many providers are also combining Robotic Process Automation (RPA) with AI to increase the productivity of their care coordination teams. Using NLP to identify specific care gaps like risk-adjustable HCCs or quality measures like colon cancer screenings, care coordinators get more timely alerts when updates to advisories and notifications are needed. Using RPA to handle those repetitive tasks means care coordinators can quickly push out updates and reduce the amount of time spent on manual work.

    Provider organizations don’t need to wait to start meaningfully using AI to support their value-based programs. There are plenty of opportunities to take advantage of right now, and Edifecs is helping providers do just that.

    For every 5 charts analyzed, Edifecs’ AI-powered Risk Adjustment Clinical Suite suggests an average of 1 additional HCC for clinicians to review, helping our provider customers improve RAF accuracy by up to 15%. Our solutions deliver cost efficiencies that can help your organization secure further investment in value-based programming and stimulate organizational growth—and that leads to better care delivery and management.

    AI has a bright future in healthcare; we can help make your organization’s future just as bright. Get in touch with our team today to learn more.

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