This past week, therapy provider stakeholders met with HHSC officials and rate analysis personnel in order to discuss and present arguments about what is being perceived as certain proposed rate reductions in therapy reimbursement codes and policy changes to therapy authorizations and billing as indirectly dictated by Rider 50 of HB 1 passed in the last Texas legislature.
An important, but auxiliary issue is the (seeming?) changing of the guard of commissioners of HHSC and their staff. Kyle Janek resigned early last week, just after Chris Traylor announced and was celebrating his retirement from being Deputy Commissioner. Instead, it seems, Mr. Traylor was cajoled to stay on and accept the Commissionership for HHSC. This change raises the obvious question for the therapy business – does this signal any positive change with HHSC’s “listening mode” to therapists and therapy advocates? This remains to be seen, but past behavior may dictate some prognostication, as well that we consider the current political hit to its reputation taken by HHSC from its recent contractor problems and possible future litigation with some of its past top brass players. It is perceived that the current HHSC top tier has always taken the philosophy that therapy businesses enter into the marketplace (Medicaid and otherwise) knowing the limits of their budgets and the costs of implementing their service delivery model, and hence, should always be prepared for and evolve with reductions.
Synerimages (SHC) through the participation of Beverly Sepulveda in these meetings (and over two years ago as well) with others had offered alternative policy changes that would lead to a budget savings for therapy dollars spent by the State. If implemented correctly and fairly, the $25M/year savings would be easily achieved from these policy changes. There is even a plausible scenario which, if these policies were immediately and fully implemented and harmonized across the different therapy delivery models, would save the remaining $50M/year projected shortfall. This would mean that a rate reduction would not be necessary. As an important aside, the TAMU study (Gregory, Ohsfeldt, Lorden, & Nwaiwu, 2015), who’s mention was taken out of the Rider language of HB 1, and HHSC’s interpetive report on it (TxHHSC, 2015b), are still being directly utilized in these calculations by HHSC. This is transpiring despite the many irregularities and inaccuracies found in both papers, as pointed out here and elsewhere.
There are many problems with the quasi-proposals from HHSC regarding the “split” approaches of rate reduction and policy changes. One such problem is the implementation of a true metric for measuring in real time, the actual savings being realized by policy changes as they are rolled out. Additionally, if such shortfalls are more than neutralized by the policy rollouts, any excess savings would not be credited back to the $50M/year split for rate reductions and hence, no upward adjustments to the fee schedule would be made.
Additionally, there are various ways to “carve out” rate reductions to achieve the $50M/year savings, requiring the latest baseline numbers (2015 pro-rata). Our earlier calculations show that for the 2016-2017 biennial, a one-time across-the-board rate reduction of approximately 5.7% to all delivery models, would be more than adequate to achieve those savings, accounting for modest Medicaid roll increases (something HHSC proposals do not explicitly do), and assuming the policies achieve, at the least, the $25M/year savings. Nonetheless, there are complications to these assumptions that have nothing to do with the analysis. One of the proposals from HHSC to the legislators mentioned the possibility of smaller graduated reductions and the conversion of HH untimed to timed billing (TxHHSC, 2015a). The assumption used for this last conversion was that the average HH therapy treatment visit was billed for 3 or fewer units. Using that number, HHSC had calculated savings of over $6M/year. However, it is nearly impossible to estimate that number as a therapy claim does not include any timed information (however, the required therapy document contains time parameters).
An office setting could corroborate timed treatments using the sign-in sheet times and the required therapy documentation of time-in and time-out. These, of course, are not entirely foolproof as, in essence, any clinical documentation is not. Nonetheless, a timed parameter in claims, together with an electronic time verification system and further patient verification of a visit time period would present a much better metric for therapy units billed.
A key component in HHSC’s pattern here is their expressed possibility of graduated rate reductions and of implementing one scheme at a time. Hence, the probability of a smaller initial rate reduction to be implemented starting on 9/1/2015, with a proposal announcement on or around 7/10/2015 and a public hearing on 7/20/2015 is clear. This rate reduction may then be followed by yearly rate reductions of the same or similar magnitude. The policy changes would then be implemented sometime during the beginning of 2016, most probably on 1/1/2016 with announcements coming in November and/or December of 2015.
Historically, we must look at the compounded effect of repeated rate reductions instead of each one as an additive effect. For example, starting at some year, say 2005, when the therapy fee schedule was essentially at its peak rate, since 2010, there have been substantial 2 or 1-year reductions. The effective percentage of 2005 rates after n years would then be the product P(n) = (1-r1)(1-r2)…(1-rn), where ri represents the rate reduction for year i after 2005. At year n after 2005, the effective rate reduction since 2005 would then be 1 – P(n). You will then notice that the effect is multiplicative and not additive. So, when measuring the effective rate reduction during a period of time, it will be smaller than adding all individual rate reductions that were implemented during that period of time. For example, if there were two consecutive yearly 5% rate reductions, the effective rate reduction during that period would not be 10%, but 9.75%. This will be important if HHSC decides to implement consecutive, but smaller rate reductions during a time period, (i.e., 2 to 5-year periods of consecutive reductions).
As a side note, the rate reduction proposal announcement will likely coincide with SynerImages’ therapy facility conference on 7/10/2015 in San Marcos, Texas. There will be HHSC representation in this conference. While there is no guarantee that the rate proposal will or can be discussed with them, this is an opportunity to analyze that all important fee schedule proposal and policy changes with others. Please consider attending the conference next month. Visit our website at http://synerimages.com/events-links.html to register for the event and a hotel room. We will be offering discounts for anyone registering in the next week’s time. For your PTs, SLPs, OTRs, and home health administrators, we will be issuing 10.5 CEU(CUU)s and 8.5 administrator CEUs respectively for only 1 1/2 days attendance. You will receive the best, most accurate and updated information on the rate and policy change proposals, survey, compliance, financial, and business strategies, new alternative hybrid business delivery model diversification, legislative summaries, MCO authorization changes and processes, and general discussions on the future outlook of therapy businesses in Texas.
Continuing, there is another very curious aspect to the proposed 10 policy changes (see earlier discussion on the blog for those items). If they are to be implemented in 2016, then since most MCOs are already implementing variants of those changes and the state will be implementing managed Medicaid 100% by then, the policy changes become semi-moot, as the MCOs will be dictating their implementation details to their own liking. MCOs are allowed to vary their policies in order to achieve their savings objectives. Access to care issues will then take on another color as they would now be based on MCO policies and HHSC’s allowance of such.
This brings us to the game (theory) part of this back and forth process of rate and billing policy maneuvering between the State, providers, and the affected citizenry. A few weeks ago, the brilliant mathematician John Forbes Nash, along with his wife, were tragically killed in a car crash (taxi ride) in New Jersey. He was ironically, returning from Europe where he had received yet another major prize for his mathematical works. John Nash is most famous for his equilibrium theorem (Nash Equilibrium) about mostly rational decision makers in groups collaborating to implement strategies in uncooperative games that would guarantee that either side would not differentiate from a strategy profile in order to gain an advantage in the future. This essentially meant that a group could implement a certain strategy profile, using their constraints, and be nearly guaranteed that the other side would not deviate from theirs in order to improve their status, given that all are rational or nearly rational players, (i.e., they want the optimal outcome for themselves).
Variants of this theorem are still being utilized today in economics, war strategies, and other business development theories with the proviso that humans work in boundedly or nearly boundedly rational ways. This means that they use combinations of heuristic judgment, intelligent prediction (Bayesian probability), and intuition when they do not have complete information and/or have muddled information on the game they are involved in. There is also the condition of how and what one values something currently over what they will be in the future based on current information (discount rates).
The most famous of simple games is the iterated prisoner’s dilemma game (IPDG) in which two prisoners are separately and repeatedly interrogated in the hopes of the prosecutor of having one prisoner rat out the other. There are consequences to each prisoner for ratting out the other or playing the martyr. Some famous computational strategies to play this game are (1) tit-for-tat (TFT), defect after being double-crossed, do not cooperate otherwise, (2) never cooperate/defect, (3) always cooperate/defect, and (4) mixed strategies of (1), (2), and (3). It turns out that “the rat” is the better strategy in many situations (discounted games).
The obvious game being played in our therapy rates scenario involves the following distinctive and competitive player groups: (1) the payors (HHSC, state legislators, MCOs, the taxpayer, and the voter), (2) the providers (all three therapy delivery models and their gateway referral sources), and (3) the client (Medicaid families). Payors are constrained by budgetary and legal requirements including projected state budgets and past legal cases (SCOTUS and Texas Supreme Court access to care lawsuits and federal requirements of state Medicaid programs). Providers are constrained by their respective budgets, expertise, location economies, and mission. Clients are constrained by their budgets, their health and availability status, and their compliance issues. Additionally, within each therapy delivery model and within a therapy business’s group of local and/or regional competitors, there are prisoner’s dilemma subgames (subgames have a detailed definition in game theory). Advocates for each type of therapy delivery model present to HHSC and legislators (during the session) their respective version of what that delivery model is worth to the populace and their deserving rate schedule (or rate reduction probability and scale). Inevitably, one “rats” the other out or in an iterative way, boundedly rationalizes itself. Competitors are always trying to figure out how to survive or surpass you in your marketplace through services offered, payor portfolio changes, staff changes, marketing schemes. and, unfortunately, either consciously or not, going to the edge with compliance issues. Compliance issues are skirted to some degree and on a spectrum by all competitors in this scenario, including HHSC, the OIG, TMHP, and MCOs. What if instead, there is a cooperative nature to these negotiations, one not involving direct comparison, but instead an overall everyone-is-a-winner, trust-but-verify approach?
With respect to fees, it turns out that the only way the provider player (or group coalition) can implement a boundedly rational version of the Nash equilibrium is to cooperate with each other and implement an everyone-is-a-winner strategy for fees (Agee and Gates, 2012). However, this depends on the assumption that all sides know that others have extortionate strategies to use in case they know for sure that others do not. This proviso was proved more recently in evolutionary IPDGs with variants of the TFT strategy called ZDGTFT-2 and EXTORT-2 (Press & Dyson, 2012). In more detail, this means that a provider fee schedule should also be dictated by the constraints and strategies of the other players in such a way that they respectively “feel” (boundedly rational) as if they came out on top in the end and that they know everyone else can try and extort others with successful strategies (Adami & Hintze, 2013, Hilbe, Nowak, & Sigmund, 2013, Stewart & Plotkin, 2013).
This may mean that such a strategy to negotiate and/or form fee schedules should be based on a diverse payor portfolio (do not depend too much on a particular payor and/or client source) and on a non-fee-for-schedule (non-FFS) approach. Payors feel vindicated that they are receiving the best or nearly best possible services from providers for the least amount of reimbursement, given that they assume some non-compliance will happen. Providers feel that they are receiving the best possible reimbursement, under the circumstance, for their expertise, given that payors will be making a disproportionate profit off of their services and the taxpayer. Finally, therapy businesses will feel that they are getting the most in comparison to their competitors based on operational changes, payor contracts negotiated with respect to others rates, and surviving until the others drop out. In the end, payors have an asymmetric advantage over providers in this epoch of healthcare finance. Providers will be getting paid based on performance and client outcomes as opposed to fixed fee or capitated schedules. Hence, payors have more extortionate strategies to implement. The major risk for payors is the threat of lawsuits based on access to care issues (not Medicaid reimbursement amounts as one cannot sue states over this one issue) and on contract non-compliance with HHSC. HHSC’s risks are mostly political backlash and access to care lawsuits through provider network depletion.
This non-FFS strategy could be based on therapeutic outcomes and the implementation of consensus-driven evidence-based treatment services. This could also equate to prioritizing those therapy codes that are most important and evidence-based considering the protocols used for billing them. A prototype therapy fee schedule could then be based on awarding (increasing reimbursement amounts for) therapy codes that make the most difference in the client, while penalizing those codes that are secondary (while still important) to those outcomes. Based on this approach to a variant and dynamic reimbursement schedule, reimbursement for some codes on some claims would be larger than others based on the outcomes, discharge prognosis, reasonable maintenance or improvement status of chronic clients, etc.
For example, for speech therapy treatment code 92507, if the baseline reimbursement is $x, then on claims with a 92507 line item, in which speech treatments used are evidence-based and outcomes are extraordinary (compared to baselines), the reimbursement would be $(x+y), where y is an amount depending on the diagnosis type(s), level of severity, functional levels, protocols used, and outcome maintenance or improvement rates, among other therapy metrics. Additionally, timed units may or may not be used based on the payor philosophy on the treatment protocol.
Obviously, these are all theoretical scenarios and would depend on many conditions that would be decided by clinicians, claims processors, insurance underwriters, legislators, and agency fee analysts. These scenarios could be experimented with in a “petri dish”, (i.e., a limited area within an HHSC region with limited payers and providers). We strongly believe that within the pattern of budget constraints, state and federal politics, and agency rate policies, the current state Medicaid trend will lead to a point of diminishing returns in which lawsuits, access to care problems, and therapy business closures will prevail with no immediate compromise arising. This does not have a good ending for any of the players, even in a current Nash Equilibrium. One can still lose a lot while being in a Nash Equilibrium, still knowing that strategies cannot be improved.
Adami, C., & Hintze, A. (2013). Evolutionary instability of zero determinant strategies demonstrates that winning isn’t everything. Cornell University Library. arXiv:1208.2666v4 [q-bio.PE].
Agee, M. D., & Gates, Z. (2012). Lessons from game theory about healthcare system price inflation: Evidence from a community-level case study. Appl. Health Econ. Health Policy, 11, 1, 45-51.
Gregory, S., Ohsfeldt, R., Lorden, A., & Nwaiwu, O. (2015). Review of Texas Medicaid Acute Care Therapy Programs: Interim Report, Research Questions 1-4. Rev 1.0. Texas A&M University Health Sciences Center, Department of Health Policy & Management, School of Public Health.
Hilbe, C., Nowak, M. A., & Sigmund, K. (2013). Evolution of extortion in iterated prisoner’s dilemma games. PNAS, 110, 17, 6913-6918.
Press, W. H., & Dyson, F. J. (2012). Iterated prisoner’s dilemma contains strategies that dominate any evolutionary opponent. PNAS, 109, 26, 10409-10413.
Stewart, A. J., & Plotkin, J. B. (2013). From extortion to generosity, evolution in the iterated prisoner’s dilemma. PNAS, 110, 38, 15348-15353.
TxHHSC (2015a). Cost containment discussion and proposals: Medicaid acute care physical, occupational and speech therapy services.
TxHHSC (2015b). Review of Texas Medicaid acute care therapy programs. Strategic Decision Support, Texas Health and Human Services Commission.