This document is submitted by the Massachusetts Health Data Consortium (MHDC) and its Data Governance Collaborative (DGC) in response to the CMS CMMI Episodic-Based Payment Model RFI (CMS–5540–NC) posted in the Federal Register on July 18, 2023 and found here: https://www.federalregister.gov/documents/2023/07/18/2023-15169/request-for-information-episode-based-payment-model

About MHDC

Founded in 1978, MHDC, a not-for-profit corporation, convenes the Massachusetts’s health information community in advancing multi-stakeholder health data collaborations. MHDC’s members include payers, providers, industry associations, state and federal agencies, technology and services companies, and consumers. The Consortium is the oldest organization of its kind in the country.

MHDC provides a variety of services to its members including educational and networking opportunities, analytics services on both the administrative and clinical side (Spotlight), and data governance and standardization efforts for both clinical and administrative data (the Data Governance Collaborative/DGC and the New England Healthcare Exchange Network, respectively).

About DGC

The DGC is a collaboration between payer and provider organizations convened to discuss, design, and implement data sharing and interoperability among payers, providers, patients/members, and other interested parties who need health data. It is a one stop interoperability resource. The DGC primarily focuses on three areas:

1. Collaboration: Development of common understanding of and specifications for data standards, exchange mechanisms, and what it means to participate in the modern health IT ecosystem
2. Education: helping members understand their regulatory obligations, the data and exchange standards they're expected to use, and modern technology and related processes
3. Innovation: Identification and development of projects and services needed to make modern health data practices and exchange a reality

General Comments

This section general comments that cross multiple questions asked by CMS or that do not have specific questions in the RFI.

Out of Network Providers

Our Data Governance Collaborative had a lively discussion on this topic and concluded that in most cases all providers of services throughout an episode of care must be in-network providers. As soon as an out of network provider is used the entire bolus of care should be kicked out of the episodic payment model into more traditional fee for service. Our participants feel out of network providers cannot be forced to participate as they did not sign any contracts with the payer. One possible exception to this is if there is no in-network provider able to provide services for the patient, but it is unclear exactly how this should work.

We note that No Surprises Act requirements that services provided at an in-network facility by an out of network provider be treated as in-network services may help, but only if that is extended to the reimbursement expectations as well as to patient responsibilities. As an aside, we would welcome specific regulation regarding the exact rules/extent of how this clause applies to allowed charges and payment responsibilities.

Network Adequacy Requirements

Related to the immediately previous comment, our Data Governance Collaborative also feels strongly that the network of available providers for services deemed essential to an episode of care be a factor in whether the episode of care can be enacted. If a particular specialist is required for services and the network has no specialists of that exact type, then patients in that particular plan should not be eligible for treatment under the episode of care (unless, per some of our thoughts in the previous comment, they can be required to accept payment within the strictures of the episode of care rules).

Availability of Services

Participants in our Data Governance Collaborative note that there can be several reasons for a delay of services that could make a timeboxed episode of care problematic. Availability and staffing are major issues. If an episode of care is initiated by an ED visit that requires follow up in-patient care, there could be a delay of days before such a bed is secured for the patient. The patient remains in the ED and continues to receive care, but it is not at the level of treatment intended to be covered by the episode of care. In cases of this sort, our DGC believes the clock on the episode of care should not start until a bed is located and the patient is transferred to begin that phase of care.

Similarly, there is an ongoing staffing shortage in a wide swath of healthcare. This may affect appointment availability for a variety of services. If a patient is supposed to get two appointments for X as part of their episode of care but the providers of X don’t have any availability for three months, does the episode get stretched out or does the bundle of services fall out of the episode of care and get charged via more tradition means?

In both of these cases, there is or may be interim care that’s both less helpful to the patient and more expensive than the intended services. The question of how the additional days in an ED or extra ED visits made in lieu of seeing the expected specialist who had no appointments available is one that needs to be addressed. Sadly, we do not have the answers to these problems or how they do or should affect how bundles of care billed under an episode of care model for a patient experiencing them.

Alignment of Data Requests

Our Data Governance Collaborative notes some frustration with being required to send mostly the same data in similar but not identical exchange mechanisms over and over again to different partners for different programs. We recognize that using USCDI/US Core-based FHIR exchange should significantly help this issue, but alignment of equity data collection, quality measures data, and other relevant data across different CMS programs (and with NCQA and other industry partners) would be extremely helpful.

Response to Specific Questions - Care Delivery and Incentive Structure Alignment

This section will list specific questions asked in the care delivery/incentive structure section of the RFI and provide our responses to them.

What should CMS consider in the design of this model to effectively incorporate health information technology (health IT) standards and functionality, including interoperability, to support the aims of the model?

Our Data Governance Collaborative believes some or all of the following would be helpful:

• Use of full USCDI/US Core data exchange between all relevant players to provide a richer data set to address clinical needs, equity concerns, and set appropriate risk.
• Incorporation of standard digital quality measures (either eCQMs or dQMs) as an option (as an interim step before eventually requiring their use) would be one way to encourage better integration with existing health IT and encourage interoperability as a mechanism for standardization, consistency, and burden reduction.
• Ability to personalize some digital quality measures to better suit the individual needs of specific patients based on the other data available and patient preferences for treatment
• Use of standardized collection tools, reporting mechanisms, patient attribution mechanisms, and similar, perhaps leveraging some of the newer DaVinci implementation guides covering these areas (including the output of new Value Based Care Performance Reporting workgroup once adopted)
• Partnering with ONC to stand up certification against the requirements of specific models (data expectations, quality measures support, SDOH and equity data collection, etc)

How can CMS include home and community-based interventions during episode care transitions that provide connections to primary care or behavioral health and support patient independence in home and community settings?

Several payer participants in our Data Governance Collaborative noted that the current payment models for home care and community-based interventions are structured completely differently from more traditional care models and it would be difficult to incorporate them into a combined episode of care payment model. We do not have any specific suggestions but agree that it is important to determine some mechanism to incorporate these services if one of the goals is equity as underserved and disadvantaged populations are more likely to use, need, and benefit from these types of services.

Response to Specific Questions – Clinical Episodes

This section will list specific questions asked in the clinical episodes section of the RFI and provide our responses to them.

Should CMS consider alternatives to a 30-day episode length? If so, include evidence to support this marker as the most appropriate transition point from the hospital to the primary care provider.

Our DGC participants have seen 30 day, 60 day, and 90 day episodes of care be implemented. In many cases, the length is dependent on the types of services included in the episode and the triggering event. For example, an orthopedic surgery will likely require more than 30 days of post-surgical physical therapy. If these services are part of the episode, the episode must be longer or suffer from an artificial time-based division of how services are compensated which adds considerable burden on the PT.

We favor having some flexibility on the episode length based on the type of triggering event and previous data showing the typical length of care needed afterward (plus some buffer given that individuals are different and will not all fall within even a standard deviation of the mean). If episodes are designed to be specific to a particular trigger, gathering and analyzing the necessary data to determine this time frame should be part of the preparation of the model. If not, allowing for flexibility in length provided it can be backed up by data should be part of the model.

We also note that network adequacy and industry issues such as staffing shortages may cause delays in care (see comments above); CMS should carefully consider how these are addressed within payment models so as to ensure equitable distribution of funds and that services are not being paid twice – once because they were anticipated to be part of an episode and once later when they’re actually provided.

The specific trigger may also play a role in how long an episode of care should last. For example, a patient may be under observation but some event triggers the need for more active care. We posit that any episode of care for that event should start at the triggering event when the more active care begins and not at the start of the observation period even though those may be part of the same visit/hospitalization/billable event from the patient perspective. If CMS chooses to start the event at the start of the observation period, a longer episode may be needed to ensure the patient receives the expected standard of care for that episode.

We recommend CMS look at data around how long more intensive post-hospital/post-surgery/post-event care happens for the event triggering the episode and build some guidelines around that (while remembering that patients are individuals and many will need more time than the average needed across a population). For example:

• If an orthopedic surgery includes post-surgical physical therapy, how long does it typically last?
• If patients typically get home nursing care or home physical therapy after a certain type of hospitalization, how long does it typically last and what type of activities are typically included?
• If a patient typically goes from hospital to home care to outpatient care for a type of service, are all three stages part of the bundle and, if so, how long is the full cycle of care expected to take?

We urge CMS to think about and consider other situations that might be similar or involve similar patterns of care.

Perhaps the episode could be tied to a specific number or type of services rather than a timeframe for services that may need to continue over time (such as physical therapy). For example, if the typical patient needs 12-15 PT visits after a specific type of orthopedic surgery with 90% of patients dismissed by visit 18, the episode of care could cover up to 18 visits. This would also help address issues with staffing and appointment availability noted in a comment above.

Response to Specific Questions – Participants

This section will list specific questions asked in the participants section of the RFI and provide our responses to them.

Given that some entities may be better positioned to assume financial risk, what considerations should CMS take into account about different types of potential participants, such as hospitals and PGPs?

Our DGC participants note that when taking on risk the size of a PGP matters a great deal. Do there need to be different programs for different types of practices to account for this? We did not come up with any specific suggestions around this, but want to make sure that CMS is considering it during its program design discussions.

Response to Specific Questions – Health Equity

This section will list specific questions asked in the equity section of the RFI and provide our responses to them.

What risk adjustments should be made to financial benchmarks to account for higher costs of traditionally underserved populations and safety net hospitals?

Providers that typically serve “sicker” patients must be given more leeway on the financial benchmarks. One suggestion our Data Governance Collaborative had was perhaps developing three models for risk calculations:

1. A standard model that generally applies
2. A safety net/critical access hospital model that assumes patients are more complex/”sicker”/perhaps not getting regular care they need
3. A high SDOH model for hospitals with high levels of patients with specific diabetic, hypertension, cancer and other identified diseases that has committed to spending a portion of their funds on SDOH interventions for these patients

This type of program might also incentivize providers to collect SDOH information routinely and reliably. We note we believe that USCDI-level collection including not just Z codes on claims (which are extremely limited) but full collection of concerns, assessments, goals, and interventions should be prioritized. At the same time, collecting the data just to have the data does not lead to a good patient experience so tying it to getting better financial incentives if you have actual programs should lead to a better patient experience around SDOH.

A model that gives more leeway to providers with significant patient populations with both mental and physical health issues to address may also be appropriate. The general consensus among DGC participants is this combination can exponentially increase costs, so any provider with a disproportionately high percentage of such patients will likely be at a financial disadvantage. However, we are not suggesting creating this model at this time because there may be some difficulty getting sufficient access to behavioral health data to accurately make these assessments. In addition to restrictions on sharing behavioral health data, so much behavioral health data is not paid for by patient insurance and is difficult to uncover. HRSA and SAMHSA may have access to some of this data that could be leveraged; we suggest CMS consider partnering with them to at least explore the idea.

What other factors could be considered for providers who serve underserved beneficiaries or beneficiaries who experience social risk factors?

Although we noted the possibility of having a separate risk model for participants committed to addressing SDOH needs of their patient population, another option might be to just directly give additional funds to programs that address SDOH needs. This could take the form of having an onsite food pantry, providing transportation vouchers to patients, or other programmatic efforts offered directly by the provider.

We also recommend providing a financial bump of some sorts to providers likely to have higher than average language translation needs to better serve non-native English speakers and to not disadvantage those providers likely to encounter them within their patient populations.

We are not familiar with any (beyond perhaps having a disability specialist within the patient services department), but providers that offer specific programs to directly serve disabled patients should also be considered in similar ways to any more general SDOH-focused programs noted above.

Aside from claims data, what data sources would be valuable for evaluation and tracking of health equity?

Claims data is not particularly useful for tracking health equity in our experience. Z codes are limited and demographic data is rarely included. We strongly recommend including clinical data exchange meeting at least USCDI v3 (now that it’s officially supported by ONC under the SVAP program) as part of all programs.

What data or metrics or both should we share with participants to ensure they are addressing gaps in clinical outcomes and access to appropriate procedural care and with what frequency?

What data or metrics or both should we share publicly to help inform beneficiaries of provider performance?

We believe reporting sliced by demographic data (race, ethnicity, religion, language, disability, etc) and also by geography (comparisons among providers in the same physical area) as well as modality of care (phone, video, onsite, home, etc.) would all be helpful.

In addition to CMS analysis, we suggest de-identified data related to these areas would be extremely helpful to AHRQ and other research agencies and sharing with them may also greatly increase the level and type of analysis made available about the data.

In addition, some of our Data Governance Collaborative participants note that CMS can sometimes take some time to provide public reporting of other data (one example given is data related to Stars ratings). Anything CMS can do to reduce the time between data collection and public reporting using it would be helpful.

Response to Specific Questions – Quality Measures and Multi-Payer Alignment

This section will list specific questions asked in the quality measures section of the RFI and provide our responses to them.

What quality measures are other payers using to drive improvements in clinical episodes?

This is not quite the question asked, but our Data Governance Collaborative participants (all well versed in quality measures) felt the following quality measures should be collected for an episode-based model:

• Prevalence of follow up services related to the initiating event. For example, did a patient get cardiac care after a heart attack or continue mental health care after an acute episode.
• Measurement of ideal timeline for follow up vs when it actually happened. One concern here is if the measurements can/should include care that happened after the episode of care has expired.
• Patient experience indicators. For example, do they feel cared for or just dumped to the next care provider?
• Measures of financial health/outcomes of episode payment vs. what would have been paid via traditional FFS payments

We note there was a lengthy discussion of the usefulness, accuracy, and low response rate of patient surveys and a reluctance of some participants to give it significant weight. Some of the concerns raised include:

• Given the low rates of response, how reliable is patient response data? [in the experience of those present for the discussion, survey modality does not make any difference in the low response rate; it is about the same for paper, email, online, phone, etc.]
• Are patient surveys designed to capture the views of disadvantaged patients? Do they account for various disabilities, inability to speak or read English, etc?
• Are people only taking the time to reply if they were unhappy?
• Conversely, are people who are unhappy afraid to reply because they think it may adversely affect the care they’re still undergoing and need to continue?
• People are reluctant to reply because they don’t understand how the data will be used
• People are reluctant to reply because they’re afraid it will be part of an adverse security event and publicized without consent

We did not come up with any major solutions, just a caution to take patient survey data with a grain of salt. The participants with survey experience noted that the responses to their surveys tend to be toward the extremes – people who were very happy or very unhappy with their care.

The CAHPS® for the Merit-based Incentive Payment System (MIPS) includes questions to assess the degree to which shared decision-making has been implemented in the outpatient setting. How can CMS most effectively measure these activities in the hospital setting?

This question did not make sense to our Data Governance Collaborative participants who felt that the same exact system and questions should work in any setting and just be used as currently designed in a hospital setting. This included one participant who serves on a CAHPS committee and is very familiar with its design and contents.

What supports can this new model provide for decreasing burden of data collection?

How can registries, electronic health records, and other quality reporting systems reduce reporting burden for participants?

We recommend the following:

• Use FHIR standards as much as possible
• Use Industry-adopted FHIR implementation guides as much as possible
• Leverage SMART on FHIR apps for data collection as much as possible
• Encourage ONC to stand up certifications related to specific models so providers in those models can leverage their EHRs to provide the needed data
• Align measures across programs and with NCQA as much as possible
• Define specific quality measures that everyone in the program must use, or have a set of measures where payers must choose 6 of 8 or select from a manageable menu of options from column A, column B, column C, etc to ensure coverage within areas CMS particularly cares about
• Recommend or require the use of specific SDOH questionnaires (but ensure they are not biased or designed to only consider the needs of a subset of the population, such as questionnaires that only ask about food insecurity within the context of not being able to afford food and not considering it can be caused by other things)

Response to Specific Questions – Payment Methodology and Structure

This section will list specific questions asked in the payments section of the RFI and provide our responses to them.

How can CMS account for apparent changes in risk that are actually due to changes in coding patterns rather than changes in health status?

This is a really sticky area. Our Data Governance Collaborative participants noted that risk adjustment is extremely sensitive to coding patterns. On one hand, there needs to be safeguards to ensure that upcoding doesn't happen but at the same time providers must be able to code for the legitimate conditions without fear for being dinged about it. All of the norms and standards are based on averages and expectations that patients generally fall within a standard deviation or so from those averages, but when dealing with individuals there are always outliers. Some patients will legitimately have a lot of intensive conditions that affect treatments and should be included in coding related to it.

One mechanism that seems obvious is to use clinical data to support diagnoses and coding choices used for specific procedures or events. If a patient is coded as having diabetes and hypertension and colon cancer and asthma and whatever else, use clinical data collection to verify that the patient is being treated for all of those conditions, or at least that they are noted in the patient’s history if not under active treatment. Preferably this would be done using data collected via standard operational data collection means such as US Core data exchanges over FHIR or perhaps via USCDI-compliant C-CDA documents (until everyone is ready for FHIR exchange).

Response to Specific Questions – Model Overlap

This section will list specific questions asked in the model overlap section of the RFI and provide our responses to them.

How can CMS allow beneficiary overlap with ACO initiatives yet ensure Medicare is not double-paying incentives for the same beneficiary?

Our Data Governance Collaborative agrees that the best way to do this is to incentivize interoperability efforts and consistent sharing of standardized data that drives collaboration and reduces redundancies.