Collaborative artificial intelligence system for investigation of healthcare claims compliance.

Healthcare fraud, waste and abuse are costly problems that have huge impact on society. Traditional approaches to identify non-compliant claims rely on auditing strategies requiring trained professionals, or on machine learning methods requiring labelled data and possibly lacking interpretability. We present Clais, a collaborative artificial intelligence system for claims analysis. Clais automatically extracts human-interpretable rules from healthcare policy documents (0.72 F1-score), and it enables professionals to edit and validate the extracted rules through an intuitive user interface. Clais executes the rules on claim records to identify non-compliance: on this task Clais significantly outperforms two baseline machine learning models, and its median F1-score is 1.0 (IQR = 0.83 to 1.0) when executing the extracted rules, and 1.0 (IQR = 1.0 to 1.0) when executing the same rules after human curation. Professionals confirm through a user study the usefulness of Clais in making their workflow simpler and more effective.

Learning Insurance Benefit Rules from Policy Texts with Small Labeled Data.

To protect vital health program funds from being paid out on services that are wasteful and inconsistent with medical practices, government healthcare insurance programs need to validate the integrity of claims submitted by providers for reimbursement. However, due the complexity of healthcare billing policies and the lack of coded rules, maintaining "integrity" is a labor-intensive task, often narrow-scope and expensive. We propose an approach that combines deep learning and an ontology to support the extraction of actionable knowledge on benefit rules from regulatory healthcare policy text. We demonstrate its feasibility even in the presence of small ground truth labeled data provided by policy investigators. Leveraging deep learning and rich ontological information enables the system to learn from human corrections and capture better benefit rules from policy text, beyond just using a deterministic approach based on pre-defined textual and semantic pattterns.

Discovering New Social Determinants of Health Concepts from Unstructured Data: Framework and Evaluation.

Social determinants of health (SDoH) are the complex set of circumstances in which individuals are born, or with which they live, that impact their health. Integrating SDoH into practice requires that information systems are able to identify SDoH-related concepts from charts and case notes through vocabularies or terminologies. Despite significant standardisation efforts across healthcare domains, SDoH coverage remains sparse in existing terminologies due to the broad spectrum of this domain, ranging from family relations, risk factors, to social programs and benefits, which are not consistently captured across administrative and clinical settings. This paper presents a framework to mine, evaluate and recommend new multidisciplinary concepts that relate to or impact the health and well-being of individuals using a word embedding model trained from a large dynamic corpus of unstructured data. Five key SDoH domains were selected and evaluated by domain experts. The concepts resulting from the trained model were matched against well-established meta-thesaurus UMLS and terminology SNOMED-CT and, overall, a significant proportion of concepts from a set of 10,000 candidates were not found (31% and 28% respectively). The results confirm both the gaps in current terminologies and the feasibility and impact of the methods presented in this paper for the incremental discovery and validation of new SDoH concepts together with domain experts. This sustainable approach facilitates the development and refinement of new and existing terminologies and, in turn, it allows systems such as Natural Language Processing (NLP) annotators to leverage SDoH concepts across integrated care settings.

Ontology-Guided Policy Information Extraction for Healthcare Fraud Detection.

Financial losses in Medicaid, from Fraud, Waste and Abuse (FWA), in the United States are estimated to be in the tens of billions of dollars each year. This results in escalating costs as well as limiting the funding available to worthy recipients of healthcare. The Centers for Medicare & Medicaid Services mandate thorough auditing, in which policy investigators manually research and interpret the policy to validate the integrity of claims submitted by providers for reimbursement, a very time-consuming process. We propose a system that aims to interpret unstructured policy text to semi-automatically audit provider claims. Guided by a domain ontology, our system extracts entities and relations to build benefit rules that can be executed on top of claims to identify improper payments, and often in turn payment policy or claims adjudication system vulnerabilities. We validate the automatic knowledge extraction from policies based on ground truth created by domain experts. Lastly, we discuss how the system can co-reason with human investigators in order to increase thoroughness and consistency in the review of claims and policy, to identify providers that systematically violate policies and to help in prioritising investigations.