My firm has a unique practice concentration in the area of complex litigation. Our expertise is principally on the defense side of claims. Our team assists long-term care providers in leveraging intricate data to safeguard against legal disputes and adverse public opinion. We help clients discover methods to analyze and utilize data from government, consumers, and providers to effectively advocate for the senior care facility.
Among the largest challenges we have when working with clients and providers is deciphering data, particularly government data, creating an accurate picture of the case, what occurred, the overall compliance record of the facility, the condition of the patient population, and at times, the staffing situation (not just numbers of staff but ratio of professional vs. non-professional to patients in relationship to patient needs). A good article I found on this subject was posted in McKnight’s. Its available here: Providers need ‘coherent story’ to combat data misrepresentation in legal cases, public opinion: experts warn – McKnight’s Long-Term Care News (mcknights.com)
The reality is that providers and law firms and insurers alike, don’t have an in-depth knowledge of data such as Quality Measures, Five Star Ratings, staffing data, survey data inclusive of grid/citation levels, cost report information, and CASPER measures. This public data can be readily manipulated by plaintiffs with the intent of painting a particular picture that may or may not (often not), be relevant
Classic cases in point occur via MDS coding, particularly as the same is tied to reimbursement. Terms like max assist, minimal assist, fall risk, wound stages, cognition levels, can be (and are) readily interpreted as more negative than not. MDS coding for reimbursement encourages exacting detail, capturing comorbidities and risk factors, levels of dependency during look back and assessment periods, etc. It is point-in-time. It is also an incentive to drive higher payments under Medicare tied to patient complexity and debility. In litigation, this may or may not be relevant to the case.
Litigation, however, is not the only area where data can be misconstrued. Provider quality data is used in underwriting insurance (liability primarily) and in underwriting financings. The better the risk profile, the better the credit or premium. In litigation or other business matters, providers working to defend themselves from legal claims or trying to convince banks and healthcare partners to view them favorably, often need to, and should, lean on experts within the industry, to qualify concerns that data points may raise.
Surveys and survey data are difficult for a non-industry expert to qualify. Terms such as “potential for harm” are nebulous as is “widespread” in survey speak. Moreover, this can be magnified when delayed state surveys leave providers stuck with old red flags that likely, changed since the prior survey (often, 18 or more months ago). Ideally, the provider has maintained data such as audits, on prior plans of corrections.
Within the pages on this site, there are lots of posts on risk management, data, and quality. There are also some tools available for download (audits, templates, etc.). Here’s three that I think, tie well to this post and are good reads for folks interested in this subject.
- https://rhislop3.com/2012/05/28/snfs-its-all-about-quality-now/
- https://rhislop3.com/2018/11/16/the-real-impacts-of-poor-quality-inadequate-compliance-and-weak-risk-management/
- https://rhislop3.com/2018/11/29/follow-up-real-impacts-of-poor-quality-and-lax-compliance/
One final note. Readers, please feel free to reach out to me via comment here or at rhislop@h2healthllc.com with questions or if needed, proposals for assistance with litigation, mediation, arbitration, or regulatory action. Additionally, my firm can put together education on this subject for providers or support agencies such as law firms or insurers.