3 ways AI can help streamline your MDS process
In order to be fully reimbursed for care, post-acute providers must submit the minimum data set (MDS) for each resident. The MDS is a lengthy series of questions designed to properly gauge the amount of caregiving needed to successfully care for a resident.
The Centers for Medicare and Medicaid Services (CMS) require the MDS to be completed within 14 days of admission to a skilled nursing or long-term care facility. The MDS questionnaire has several sections that cover different areas and aspects of resident care needs, and this data is also generally required to be included in the facility’s EHR.
Three main MDS challenges
What this means for your facility is that when a resident is admitted, everything needs to be documented: Diagnoses, medical history, medications, recent procedures and anything relevant needs to be entered into your EHR. Many organizations have a dedicated role for this: the MDS coordinator, who gathers the required information so your EHR becomes the “source of truth” for patient or resident data. An MDS coordinator can help mitigate three main challenges when gathering admission information:
- Complex, detailed information: The complexity of the MDS means that gathering this information requires a high degree of care coordination. The MDS coordinator may need to work with nurses or specialists to collect key information, or even ask them to book a quick appointment to check in with a resident and evaluate conditions such as wounds.
- Information interactions: There are complex interactions between all the data in the MDS forms, and those interactions can affect reimbursement. If the MDS doesn’t accurately reflect the patient or resident’s condition, the provider might not be fully reimbursed for care that’s been delivered. This sometimes happens when an MDS coordinator doesn’t see information in the charts about a specific treatment the resident might be receiving because clinicians are documenting treatments in places the MDS coordinator missed. This is why a centralized location for data, such as a robust EHR, is essential for accurate reimbursement.
- Timeliness: MDS documentation is time-consuming not just for an MDS coordinator, but for all staff members who need to gather detailed information for each resident in a facility. At a time when most post-acute care facilities report ongoing challenges related to staffing, the extra effort it takes to gather MDS data can take time away from other important tasks.
How can AI make a difference?
Large language models and other AI tools can handle complex tasks by extracting information and distilling it down to what is relevant. This same technology can be applied to filter through the complex information MDS coordinators need to review, checking for incomplete or contradictory data. For example, if a resident is taking medication for depression but doesn’t have a depression diagnosis, AI tools can flag that inconsistency so the issue can be resolved before it’s input into the MDS system. These tools can also monitor a facility’s EHR documentation and notify the appropriate caregiver or specialist if something inconsistent turns up, so documentation can be corrected. This greatly reduces the burden of understanding and analyzing each resident’s documentation to make sure it’s appropriate, properly filled out and free of any contradictions.
Managing complexity and volume
Some facilities have specialists who review interactions between the MDS questions and each aspect within the MDS and develop a HIPPS (Health Insurance Prospective Payment System) code. This code can immediately affect billing and reimbursement rates for resident care. But now, AI-enabled systems can monitor the MDS itself. They can analyze answers to questions and flag responses that might cause your facility to be reimbursed at a lower rate or run a risk of denial of payment, which can be costly and time-consuming to correct and re-submit.
What’s more, these AI tools typically can highlight inconsistencies as soon as the MDS coordinator finishes filling out the form by generating a report that shows the expected reimbursement. This technology acts as a kind of experienced MDS coordinator sitting beside you to point out mistakes or potential issues, and even to make suggestions about corrections or about including documentation for an aspect of care that could be more appropriate for the resident’s needs.
AI tools can also help with the sheer volume of MDS submissions facilities are required to complete. MDS needs to be completed for every resident that comes into a facility. And then it needs to be refreshed at least once each quarter. Using AI tools to show staff when documentation is missing or contradictory speeds up the process and makes it more efficient overall.
Evaluating nuanced data
Another way AI can help with MDS is when there’s a significant change in condition. One clear-cut example is a resident who suddenly develops a more serious condition—they fall and fracture a hip, for example—and a higher level of care is required. The provider will have to submit a new MDS to be reimbursed appropriately for the new, higher level of care.
But there are instances where the need to initiate a change on the MDS is less clear. In these cases, AI can reassess scores on elements such as mobility or nutrient intake and alert providers about subtle changes that may signal a more significant shift in condition—and trigger a need to update the MDS to make sure reimbursement levels are appropriate for the care being delivered.
Supporting efficient processes
AI tools can streamline the entire MDS process for your staff. Consider this: A typical MDS can take anywhere from one-and-a-half to three hours per admission. A typical 100-bed facility generally handles 50 to 60 admissions per month. That means facility staff might spend from 75 to 180 hours per month just on MDS. If an AI tool can cut the time needed to submit MDS data, that could save hours equal to a full-time position. And if you add to that the time saved not having to re-submit MDS due to errors, the impact is even greater.
Organizations that use AI to help manage their MDS submissions can save time and free up staff to focus on the edge cases where more information is required to ensure accurate reimbursement. Especially during a time when skilled MDS coordinators can be hard to find, using AI-enabled tools offers the potential to save time and improve reimbursement, keeping your business healthy and improving care.
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Daniel Zhu
Daniel Zhu comes from a diverse background of clinical experience and technology entrepreneurship. Having spent five years in clinical and clinical research roles with the Alberta health system and the University of Toronto health network, he has diverted his medical expertise to architecting and building technology solutions to optimize health care practice. At various organizations, he has lead engineering teams, product teams, and founded his own natural language processing start-up in the clinical research space. Stepping away from the start-up world, Daniel has spent time as a data consultant for large corporations such as Ford, Co-op, and RBI. Re-entering the health technology field, today Daniel has recently joined the ResMed and MatrixCare team to lead the productization of AI and machine learning capabilities.
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