The Healthcare Industry is defined by its best participants, practices, and entities. At the same time, the sector is often burdened by humongous amounts of different data types, scalability, and defined complexities. In addition, given that this industry operates under strict vigilance and compliance frameworks, providers must comply with a plethora of regulations both released and challenged by patients and the public alike.

With so many potential use cases, applications, and challenges within the scope of the healthcare industry, there is a need for a solution that ticks all the boxes and covers every aspect of the data and analytics journey. HDAP is that solution.

What follows are use cases where HDAP can act as a data accelerator for companies in the healthcare ecosystem:

Patient-Centric

HDAP is designed to leverage its data for all possible analytics related to the patient, which is naturally a key metric for the healthcare industry. These insights include performing analytics on data generated from identification, observation, admission, treatment, all encounters, discharge, and post-discharge care information.
And since the patient is effectively the epicenter of healthcare, HDAP intersects with a significant number of parallel entities.

Physician-Centric

Physicians rely on accurate information and real-time diagnostics. As a result, Analytics now forms part of their workflow processes. For that reason, it’s important to measure the performance effectiveness of physicians and their decision-making based on, for instance, treatment effectiveness, clinical rundowns, care procedures, individual bandwidths, etc. On a granular level, this information can be used as a reference for other physicians in case they come across a similar kind of clinical context.

Provider-Centric

Healthcare providers require the establishment of basic sanity checks to render effective services. This includes standardizing practices, data management, maintaining common identifiers for patients, managing information for registry purposes, and audit preparedness.

Drug-Centric

Analytics related to drugs and their usage, evidential analysis, managing data in a standardized manner, and, above all, establishing the context of medication are all current challenges for the healthcare industry. HDAP can identify and establish where certain drugs have been effective (both in practice and clinical trials) and apply that coded information accordingly.

HDAP can also help providers in terms of targeted drug discovery and application, provided that this data is approached objectively from the start.

Care-Centric

With the Affordable Care Act now firmly established and accepted by regulatory bodies and healthcare providers in the United States, it’s very important to calibrate and measure the level of care being applied across the whole treatment. This aspect of the treatment cycle is critical for reimbursement purposes and administrative clarity.

Operational/Administrative Effectiveness-Centric

Based on pre-defined operational metrics, HDAP’s unified data foundation can be used to identify both specific opportunities to optimize the operational processes and areas of improvement.

This function could incorporate issues like the identification of individual skills to better manage processes in a cost-effective manner and optimized usage of physician bandwidth. The functionality also includes data mapping between clinical data, operational data, and administrative data.

For instance, Administrative-Centric information like patient eligibility, claims history, previous treatments, and the flagging up of people likely to skip an appointment without advanced notice can improve provider satisfaction and cut down on revenue losses. This also allows organizations the opportunity to offer open slots to other patients, thereby increasing speedy access to care.

Data-Centric

Healthcare data is often one of the most formatted assets in the overall database schema. The structure, nomenclature, and taxonomies are created, optimized, and augmented many times during its lifecycle, all of which lead to a requirement for visibility and format verification.

In addition, its creation at different points of time and different locations under different contexts in different ways makes it extremely challenging for anyone who is responsible for confirming the actual quality of healthcare-related data. Indicative challenges related to this data include but are not limited to:

Encoding format: the same object is coded differently in different systems.

Attribute measurement: systems have data with identical meaning but use different a scale of measurement. This is of special relevance in healthcare.

Multiple sources of data: deciding which is the ‘master system’, and the criteria used to determine it.