Saúde08 novembro, 2023

Capturing the right data to power better healthcare

There are hundreds of data points created in just one interaction at the point of care. Better data capture – but also the integration of tools to ensure the accuracy and future applicability of the data captured – enables improved care quality and billing accuracy.

There are hundreds of individual data points created in just one healthcare interaction at the point of care. Often, these are stored in the electronic medical record through structured or – more often – unstructured fields where they are often left ignored until the next visit. While this information is helpful for informing future care interactions, it lacks the ability to improve immediate care quality for the patient. It’s no wonder that healthcare experts believe as much as 95% of health data goes unused.

Clinically speaking, better data capture at the point of care – but also the integration of tools and intelligence to ensure the accuracy and future applicability of the data captured – enables improved care quality and billing accuracy. These data can be combined with information captured outside of point of care diagnosis and standardized across data moments to uncover new insights using advanced analytics.

Improved documentation accuracy and financial health

At the point of care, providers are required to quickly document patient encounters; however existing tools are rigid making the process more complicated than it should. Further, clinicians are required to capture ICD-10 diagnosis codes, which then dictate how an encounter is billed and charged. There is a lot riding on the completeness and accuracy of what the clinician enters. The problem? Physicians are not taught how to code in medical school. And while this shouldn’t be expected of them, today’s clinical environment does make this skillset seem like a necessity when it shouldn’t have to be.

Instead, what they need is a smarter search tool seamlessly integrated into their EMR that allows them to search and document using terms, abbreviations, and descriptions in their own language, leveraging a proprietary built library of 1.3M+ clinical synonyms, acronyms, abbreviations, and misspellings, that can then be mapped appropriately to recognized, official ICD-10 diagnosis codes and descriptions. This solution must also help guide the clinician to select appropriate details about the diagnosis to ensure the most specified code has been selected, ensuring accelerated claims processing workflows and accurate reimbursement.

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Consolidate EHRs without losing insights

Healthcare system consolidation has been increasing over the last three decades, driving a decrease of ~2,000 hospitals from 1998 to 2021. While this undoubtably has an impact on patient care, that impact can be substantially worsened if the technology and data integration of these provider organizations isn’t handled with care.

Amid this dynamic M&A landscape – and the resulting need for interoperable and rapidly sharable insights – the Health Language platform integrates data from various sources and applies machine learning to map disparate lab, medication, allergy, problems, diagnoses & procedure data to interoperable industry standards. Once the information has been normalized to a shared, common standard, leaders can leverage these insights captured across hospitals to facilitate the creation of a true, longitudinal patient record.

Building custom cohorts to deliver equitable healthcare

Social determinants of health are vital to understanding the whole health of the patient. Having insight into these factors across your patient population can also help determine specific cohorts that may need increased support. Custom patient groups can also support ACO Program Parameters, quality scoring and analysis, and risk sharing. Requiring a mix of informaticists and clinical expertise, building custom patient cohorts to consistently and accurately analyze population health factors is resource and time intensive and often results in stale, inaccurate data.

With increased regulatory attention on value-based care initiatives, the Health Language platform can allow clinical teams to focus on practicing medicine while the system captures these insights in real time leveraging 160+ comprehensive SDOH value-sets using standard terminologies following the Gravity Standards. Providers can increase the accuracy of code groups through the creation of a single, integrated and trusted source of truth as well as author custom code groups using sophisticated rules-based algorithms to create new groups from scratch or customize existing definitions. Once in place, these cohorts power insights that allow you to deliver better health across diverse patient populations.

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