Data is an incredibly powerful tool in any industry. It allows businesses to identify trends, get to the root of reoccurring issues, make informed decisions, and ultimately solve problems. In today’s market, you cannot elevate your business without recording data, analyzing it, and implementing the key learnings from the story your data is telling.  In healthcare, utilizing your data effectively takes on a whole new meaning. Medical data processing helps identify business opportunities within your practice, but it can also assist you in improving health outcomes and potentially saving lives.

In any given practice, practitioners may be recording data from operational platforms like electronic health records (EHRs), payer records, telemedicine platforms, in addition to medical devices, public records, research studies, etc. These data sources notify clinicians of essential information, such as allergen alerts for a patient’s new prescription, incorrectly coded visits, or when a patient needs to be scheduled for a follow-up appointment. Additionally, this collection of data assists clinicians in developing predictive models to prevent health issues and guides them through providing value-based care both in and out of the skilled nursing facility. 

 

Predictive Modeling for Value-Based Care

 

For many years, medical practice has been structured around evidence-based care, or providing care to patients based upon practices that are strongly supported by science. But as we continue to step into a world that is so data-driven, many clinicians are beginning to implement more value-based care into their everyday practice. Value-based care is the idea that clinicians will improve the quality of patient care by effectively halting a health issue before it arises. This is often done through utilizing medical data processing systems to develop predictive models which help guide value-based care. According to IBM, “a traditional predictive model is a mathematical formula that uses historical information to predict the chance of an event happening in the future. Most predictive models are built to address a population.” 

For the long-term care population, physicians with easy access to medical data processing systems are essential, especially when dealing with chronic care management. Chronic care management refers to the medical services provided to a patient with one or more chronic diseases, such as diabetes, dementia, influenza/pneumonia, or even injuries. According to the National Institute on Aging, at least 85% of older adults have at least one chronic condition, and over 60% have at least two chronic conditions. For these patients, it would be incredibly beneficial to their health outcomes for their physicians to be enabled by medical data to develop predictive models and effectively halt symptoms of their chronic diseases before they even begin. 

Processing Medical Data in Post-Acute and Long-Term Care

 

In the PALTC space, data collection becomes even more complex as attending physicians see patients in multiple locations each day, over an extended period, and deal with the added billing and reimbursement intricacies of Medicare and Medicaid. These unique pain points paired with the frequent transition of patient care between hospitals and skilled nursing facilities (SNFs) often leave siloed crumbs of data for practice managers and clinicians to attempt to make sense of.  

In a study published in JAMA Network Open, the authors discuss the immense amount of information sharing required between hospitals and skilled nursing facilities. As patients transition out of the hospital setting and back into the SNF or visa-versa, accurate data is needed to provide patients with the most informed continuum of care. Their study shares that of the 500 SNFs questioned, over 80% of them noted at least 1 shortcoming of the usability of the patient data provided to them. This illustrates that, unfortunately, health data means virtually nothing if you’re unable to analyze it and implement learnings in a timely manner. However, when given the right tools to analyze health data effectively and efficiently, making informed decisions for patients becomes a lot more feasible.

Consolidate Your Medical Data with TheSNFist Suite 

At Saisystems Health, we understand the unique challenges post-acute and long-term care clinicians face on a day-to-day basis. That’s why we created TheSNFist suite, the first and only full stack of solutions and services designed specifically for PALTC practices. This includes PacEHR electronic health record, SNFConnect communication platform, revenue cycle management, mobile charge capture, and payor enrollment & credentialing.  

Capturing this wealth of information through our medical data processing systems and services is the first step in improving your business from an operational perspective, and also from a patient care perspective. Once you’ve begun capturing data, our PALTC experts will develop a custom, centralized dashboard to make the large repository of data collected from TheSNFist suite accessible, clear, and actionable for your team. 

To discover the power of medical data processing and analytics for your PALTC practice, please submit the form below and a member of our team will be in touch with you.