Use Cases

Fraud detection helps detect and prevent healthcare fraud through data and information sharing. They are generally categorized as mistakes resulting in administrative errors, such as incorrect billing; inefficiencies causing waste, such as ordering excessive diagnostic tests bending and abuse of rules, such as upcoding claims; and intentional, deceptive fraud, such as billing for services or tests that were not provided or that are undoubtedly medically unnecessary (and sometimes harmful to the patient). Fraud reduction requires effective identification of these kinds of activities—or, as we prefer to call them, “behaviors”—and targeted deterrence strategies directed at their root causes, including system issues.

A Document Management System protects your records by allowing you to have better control over who has access to specific information. In healthcare documentation communicates the what, why, and how of clinical care delivered to patients. These records allow other clinicians to understand reasons for care plans so they can continue to provide the best possible treatment for each individual.

Payment reconciliation—the ability to identify the funding status of a particular patient payment and tie it to a corresponding bank deposit—often is a distress point in what can easily be a smooth process. Yet for most hospitals and health systems, reconciliation is done manually, creating a time consuming, error prone and, frankly, unnecessary effort.

Three concepts permeate all aspects of biological life: genotype, phenotype, and environment. A digital phenotype is the moment-by-moment quantification of an individual, using data from personal digital devices, electronic health records and other data sources.

It focuses on but is not limited to clinical history, behavioral patterns, sleep, social interactions, physical mobility, gross motor activity, cognitive functioning, and speech and language production.

Medical imaging is rapidly being
enhanced with AI algorithms. Improve the traceability of the contributions of image and annotation data for diagnosis. Improve the traceability of the training of AI algorithms, thus enhancing privacy and fairness, and potentially make imaging data for AI available in larger quantities. Clinical applications of advanced technology include the documentation of the contribution of different “authors” including AI algorithms to multipart reports, the documentation of the use of AI algorithms towards the diagnosis, the possibility to enhance the accessibility of relevant information in electronic health records, and a better control of users over their personal health records.

This is the process of comparing key safety data variables between the drug (or device) serious adverse events (SAE) database and the clinical database in order to identify any conflict, determine whether a conflict is acceptable or not and, if acceptable, document the conflict. Systems can include the capability to upload non-e Case Report Form (CRF) into records to generate eCRF summaries.

Healthcare Effectiveness Data and Information Set (HEDIS®) from the National Committee for Quality Assurance (NCQA) is a widely used set of performance measures to track the health of populations often used to evaluate the quality of care.

It is used as comprehensive set of standardized performance measures designed to provide purchasers and consumers with the information they need for reliable comparison of health plan performance.

Geographic data is often associated with social determinants of health (SDoH) data and important dimensions of the health of individuals. Environmental data can include physical quantities such water and air quality or more generally refer to SDoH. Geographic data is used to also measure accessibility to care through distances or travel times to care providers.

The American Hospital Association states “Population health management refers to the process of improving clinical health outcomes of a defined group of individuals through improved care coordination and patient engagement supported by appropriate financial and care models.” Population health management depends critically on organized, relevant, data to deliver, evaluate and improve care.

Benchmarking, which is the process of establishing a standard of excellence and comparing a business function or activity, a product, or an enterprise as a whole with that standard, are becoming an increasingly more important process used by healthcare institutions to reduce expenses and simultaneously improve service, quality and outcomes. As with other industries the healthcare industry is leveraging technology to help streamline and improve the process.

Patient segmentation for case and management is increasingly important. Ancillary services to support primary care physician can consider risk based on clinical factors, but also social determinants of health (SDoH) and activation of patients. Data to understand the patient needs in multiple dimensions is important to allocation appropriate care services and improve health.

RBA is a disciplined way of thinking and taking action that can be used to improve quality of life in organization and as a way to improve the performance of programs within these organizations.

In healthcare it can be applied to enable organizations a data driven view into their finances and services so that they can focus attention on providing better care and maximizing revenue.

The disciplines of case management and referral management face challenges in areas such as funding and optimization, quality and efficient processes, lack of skilled and trained resources, poor collaboration with other health-care providers, and client relationship challenges.

Data-driven, collaborative technology can help close the gap on these challenges and provide a significant improvement to outcomes as well as being financially beneficial to all members of the ecosystem.