2. Tight collaboration between clinicians and data scientists. AI and machine learning are valuable tools in a plan’s payment integrity portfolio, but only when backed by significant clinical and analytical expertise. Moreover, the level of collaboration needed between these two groups of specialists will be higher than what health plans traditionally have put into play. That’s because effective use of machine learning requires that health plans ask the right questions of unsupervised models. Much like hospitals that are looking to hire experts to create ChatGPT prompts—for example, “Explain diabetes in simple language for a patient who has just been diagnosed”—machine learning models also need the right prompts to make data analysis more meaningful. Further, once the model detects suspicious activity, it will take a reviewer with special investigative skills and techniques to validate larger patterns of potential fraud, waste and abuse. This is an instance where both clinical and data science skills could prove essential.
Clinicians will also be vital to provider relationship management, assuring providers that even with the introduction of machine learning, payment decisions will still be made based on documented payment policy and clinical guidelines.
3. Data pipelines and tools that empower health plans to dig deeper. Machine learning models that rely only on data that originate within a single health plan will be more limited in their effectiveness than those that incorporate industry benchmark data as well as data across lines of business, geographies and provider networks. Just as these models require a continuous internal data pipeline, an external data pipeline will also be critical in spotting aberrant behavior by specialty, geography, type of organization and other factors, strengthening the health plan’s response. This ensures machine learning efforts are not performed “in a lab,” siloed from other business applications, but rather have a pipeline of rich production data to incorporate and learn from.
4. Project management. Such expertise will be critical to operationalizing work around how to leverage data science and clinical expertise to prevent fraud, waste and abuse. It will also reduce the risk of administrative burden for staff and protect member and provider satisfaction. For example, when machine learning identifies abusive billing patterns, a project manager can help close the loop with a combination of provider education and prepay analytics that spot inappropriate claims moving forward and ensure these claims are not paid. This enables the plan to more rapidly deploy solutions that eliminate breakdowns in payment integrity.
