workers compensation doctor

The residuals of post-acute residuals of COVID-19 (Sars-CoV-2) may result in compensable permanent disability for many individuals (long haulers). A recent study outlines the potential compensable sequelae. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the pathogen responsible for the coronavirus disease 2019 (COVID-19) pandemic, which has resulted in global healthcare crises and strained health resources. As the population of patients recovering from COVID-19 grows, it is paramount to establish an understanding of the healthcare issues surrounding them. COVID-19 is now recognized as a multi-organ disease with a broad spectrum of manifestations. Similarly to post-acute viral syndromes described in survivors of other virulent coronavirus epidemics, there are increasing reports of persistent and prolonged effects after acute COVID-19. The multi-organ sequelae of COVID-19 beyond the acute phase of infection are increasingly being appreciated as data and clinical experience in this timeframe accrue. Moreover, it is clear that care for patients with COVID-19 does not conclude at the time of hospital discharge, and interdisciplinary cooperation is needed for comprehensive care of these patients in the outpatient setting. As such, it is crucial for healthcare systems and hospitals to recognize the need to establish dedicated COVID-19 clinics74, where specialists from multiple disciplines are able to provide integrated care. Given the global scale of this pandemic, it is apparent that the healthcare needs for patients with sequelae of COVID-19 will continue to increase for the foreseeable future. Nalbandian, A., Sehgal, K., Gupta, A. et al. Jon L. Gelman of Wayne NJ is the author of NJ Workers’ Compensation Law (Thomson-Reuters) and co-author of the national treatise, Modern Workers’ Compensation Law (Thomson-Reuters). For over 4 decades the Law Offices of Jon L Gelman 1.973.696.7900 jon@gelmans.com has been representing injured workers and their families who have suffered occupational accidents and illnesses.

The performance of certain procedure codes could be similarly used to extrapolate the probability of future procedures. A patient undergoing a particular surgery will be at risk of requiring some additional surgery in the future. That probability is predictable, measurable, based on data at hand. Life expectancy is similar. Statistically, it should not be difficult to document a probable treatment path based upon injury, age, degree of perceived recovery from procedure, and perhaps a few other criteria. Proof of all of this is also at hand. In fact, this predicting is occurring right now, at the Centers. When a Workers' Compensation Medicare Set Aside Arrangement (WCMSA) is approved, it is because a human being somewhere has made these predictions. That human being has accumulated data, categorized it, organized it, and submitted it. Some other human being at the Centers has reviewed it, recalculated it, and reanalyzed it. And, as with all human-intensive processes, time has been invested.

That is the real issue, the passage of time. In some cases, it can require months. In some instances, much of that work is invested in a "what if" process, trying to figure out what an WCMSA "would" require "if" a settlement of the workers' compensation case could be reached. It may be hard to settle a case if the payer does not know how much it will pay and the payee cannot be told how much she/he will have in pocket afterward. The assumptions, predictions, and analytics already exist. Certainly, the issue with statistical analysis is always that there are outliers. If you are above or below average, then "your mileage may vary," and everyone understands that. The program would not produce "the," singular, absolute, "correct" prediction of future medical expense for this patient. However, it could produce a reasonably accurate prediction that is appropriate to use for this patient.

Certainly, there would be margin for error. Sometimes future medical cost would be over-predicted or under-predicted. But, it is naive to believe that is not happening today. Currently, there are a great many humans who are toiling to make similar predictions. They are just as apt to rely on averages, predictions, and projections. They are just as apt to overstate for those who are actually below the mean and understate for those who are above. But, overall, statistically, the result would be similar using the current process or the programming I propose. But, the programming I propose could be simplified to allow any payer to input the required data and immediately get a stated value. That value of "future medical" could then be understood and applied in the negotiation of settlement. And, the government should not necessarily shoulder that expense burden. Instead of a payer (employer/insurance carrier) paying a vendor for this predictive expertise (today), let the government charge the payer a fee to prepare the estimate using this computer program that is replete with that expertise.

The same database could persistently monitor its own performance. It could consider whether its estimate for a particular person was accurate; if it predicted use of a medication would continue two years, then in two years the program could note being correct or not. It could then adjust its predictions in future instances of that medication. The computer program could learn through actual outcomes to better predict future outcomes. In this method, the government recoups the cost of programming and development through a user fee. The government can downsize a significant workforce that is engaged today in human review and approval of these human predictions. The payer can pay a small process fee to the government instead of paying the commercial processor to have humans accumulate and package the data for human consideration. In the end, It is probable that an automated process would cost the payer less and expedite the settlement to the benefit of both the worker and payer. The predictions would be quicker, more consistent, and less costly. To further simplify the process, alleviate the management of that set-aside money.