Welcome to Value-Based Care, Outcomes and Reimbursements.

This is lecture a, Foundations of Outcomes and Reimbursements.

This lecture provides background information about the old HMO model.

How HMOs compare with ACOs, and

how ACOs are designed to ensure quality and reduce costs.

This lecture also takes a close look at quality measurement, attribution, and

risk stratification, and how they work together.

The lecture concludes by looking at challenges in measurement and reporting.

The learning objectives for

this lecture are to compare the strategies of health maintenance organizations or

HMOs and current accountable care organizations or ACOs.

So we understand how the lessons learned from the experience of HMOs in the 1990s

inform today's shared risk contracts and quality measurements.

Describe how quality measures, attribution, and

risk stratification work together to drive quality while reducing costs.

Outline common issues related to collecting quality and cost measures.

And discuss the administrative burden of reporting on various CMS payment models,

and insight on how practices may reduce this burden.

The models described in this lecture include a broad set of performance based

payment strategies that link financial incentives to providers' performance

using defined measures.

To achieve better value through improvements in quality and

slowing the growth, decreasing healthcare costs in healthcare spending.

As a reminder from earlier lectures, ACOs and

other value-based care models are meant to reward value.

And to achieve the triple aim of better population health,

better patient experience, and lower costs.

To achieve that aim, we need to measure and reward high quality care.

As the IOM and

ARHQ definitions reflect, quality is about getting patients the care they need.

The problems in quality often are broken down into the categories of overuse,

underuse, and misuse.

Overuse means that a patient gets services that don't provide a benefit or

where the harm is greater than the benefit.

An example of overuse would be a patient getting a prescription for

an antibiotic when the patient has a cold.

Or a patient getting a test repeated because the physician didn't have

the results from the earlier test.

Underuse means a patient doesn't get care that is needed.

For example,

if a patient didn't get needed physical therapy, that would be underuse.

And misuse means that a patient gets the wrong care.

For example, there's an error and

a patient is prescribed the wrong medication.

With that understanding of healthcare quality issues,

how should our approaches to outcomes and reimbursement address quality?

How can we measure quality and

structure payments to encourage the right level of evidence-based care?

Let's begin by looking at some history and

lessons learned from earlier payment models.

To provide context about outcomes and reimbursement in today's value-based

payment models, let's look back a couple of decades at the experience of HMOs.

In the 1990s, HMOs became a popular approach to rein in healthcare costs,

which had been growing quickly.

In this form of managed care, HMOs received capitated payments.

In other words, the HMO got paid a fixed amount per member.

And in return, the HMO provided or

arranged for all covered services for its members.

An HMO's profit or loss depended on whether it could deliver those services

at a cost that was less than the capitated payments it received.

This meant the HMO had a strong incentive to squeeze costs.

The HMOs tried to control costs by negotiating low rates from providers,

and by restricting their members' use of services.

For many providers, the negotiated rate was lower than the cost of providing care.

So those providers lost money.

For members, costs generally were good, but services suffered.

Members usually had low co-pays and other costs.

But they had to get a referral from their primary care provider in order to see

a specialist.

If members got care outside the HMO, they weren't covered for that service.

Over time, the HMO model faced a backlash from providers and

from the general public.

As Atul Gawande explains, the HMO approach to limiting unnecessary

treatment was effective in reducing health care costs.

But also had negative consequences that led to a backlash.

Faceless corporate bureaucrats second guessing medical decisions from afar

created an infuriating amount of hassle for physicians and

patients trying to orchestrate necessary care.

And sometimes led to outrageous mistakes.

Insurance executives were accused of killing people.

Facing a public outcry, they backed off and healthcare costs resumed their climb.

Given the history of HMOs, many people worry about ACOs repeating the past.

Like HMOs, ACOs involve providers taking on financial risk,

rather than simply being paid for each service provided to patients.

How do we use lessons learned from the HMO models to positively inform

the design of ACOs?

ACOs include some design elements that are meant to prevent the problems and

encourage high quality care.

Unlike HMO members, Medicare ACO beneficiaries may choose to obtain

care outside the ACO and still have coverage for those services.

The ACO is responsible for the cost of care for

its assigned beneficiaries regardless of who provides the particular service.

This provides greater coverage for beneficiaries and it reduces the incentive

for an ACO to refer patients away for high cost services.

The approach to payment also is different.

Instead of full capitation,

the medicare shared savings program ACO models use shared risk.

Because providers are at risk for only a portion of the total cost of care.

They do not face the same level of potential loss as with the HMO model.

In addition, ACOs have to meet quality measures.

These quality measures are intended to reward an ACO if it provides good care.

And penalize an ACO if it fails to provide the right care.

Quality measures were not part of the HMO model.

As a side note, part of the reason that quality measures can be used in the ACO

model has to do with technology changes between the 1990s and today.

Providers and

hospitals are much more likely to have electronic health records or EHRs.

Providing quality measures in an EHR is much less time consuming and

labor intensive than having people work through paper

records to put together quality data.

At the beginning of this lecture,

we noted that many quality problems relate to overuse or underuse.

Let's consider how the ACO model addresses those issues.

It's important to note that in addition to driving up healthcare spending,

overuse can cause serious harm to patients.

Overuse of antibiotics leads to the development of drug resistant bacteria.

Overtesting can lead to unnecessary exposure to radiation, and

thus to increased rates of cancer.

It also can cause needless stress and expense to patients.

As well as overdiagnosis and treatment of diseases when the disease poses no real

risk to the patient and treatment increases the risk of other harms.

For example, in the United States Rates of thyroid cancer detection and

removal have increased by three times in the past 20 years.

Despite that dramatic increase in detection and

treatment, the thyroid cancer death rate has not decreased.

The number of patients experiencing permanent complications from thyroid

surgery however, has dramatically increased.

By creating financial incentives to avoid overuse,

the ACO model can help address this issue.

Patients, as consumers of healthcare,

also can be encouraged to avoid overuse and choose high-value services.

We'll look at the role of consumers more in the next lecture.

Underuse was one of the key problems with the HMO model in the 1990s.

Financial incentives encouraged denial of necessary care,

as well as unnecessary or low value care.

By including quality measures as a condition,

the ACO model creates a different set of incentives.

Let's look at the checks and balances built into the ACO model.

The figure shown here illustrates how the three competing elements of quality,

cost, and healthcare experience provide these checks and balances.

In the first check and balance image, image one, ACOs could exert downward

pressure by attempting to meet cost goals by restricting care to individuals.

For example, a provider could give less care and

not charge as much in order to achieve a lower cost of care.

However, there is upward pressure also from patients

because if they are not happy with the amount or

quality of care, they have the ability to go somewhere else.

The ACO, however, is still assigned the accumulated cost for these patients.

Therefore, there isn't much reason to restrict care.

Similarly in image two, it is possible that in ACO could meet

the quality standards by over treating and spending huge amounts of money.

However, the payment model has cost measures in place

that discourage providers from over treating or

using expensive treatments that don't improve health.

Finally, in image three it is possible that

the ACO could try to meet cost goals by reducing care.

There is another check and

balance here because the quality goals of the model encourage comprehensive care.

These goals for providing care exist to assure

the providers deliver care that promotes quality outcomes.

With that background, let's define some key concepts and

take a closer look at how they work in value based models to align payments and

outcomes, so that quality is maintained or improved while costs are reduced.

Quality measures are a key part to value-based models.

Before Medicare ACO can share in any savings,

it has to show that it met required quality standards.

We'll look at some of the measures later in this lecture.

To apply quality measurement, it's important to know who was responsible for

meeting the measure.

Attribution refers to the method used to decide who is responsible for

the cost or quality of a patient's care.

For Medicare ACOs the Centers for Medicare and Medicaid Services,

or CMS, refers to attribution as assignment.

But regardless of which word is used,

the idea is to identify which providers were involved in caring for a patient and

should be held accountable for cost or quality measures.

For ACOs, attribution factors into calculation of benchmarks,

financial performance, and quality measures.

CMS publishes very detailed specifications for

its methods of assigning beneficiaries.

And those specifications are established and

updated through the shared savings program regulations.

Generally, a beneficiary can be assigned to a Medicare shared savings program

ACO if the beneficiary's primary care physician is in the ACO.

Or if the beneficiary goes to a specialist in the ACO for

most of the beneficiary's primary care.

Since beneficiaries are assigned based on their use of services during

the performance year, the ACO doesn't get the finalized list of

beneficiaries until after the end of the performance year.

The assignment rules are different in the next generation ACO model.

Those ACOs are told in advance which beneficiaries are assigned to them.

If we know what the ACO is being measured on and who is responsible for

meeting the measures, what else do we need to know?

A fair assessment of quality also takes into account how sick or

healthy the patient was to start with.

This brings us to the idea of risk stratification.

Risk stratification involves identifying the likely levels of patient health and

care needs based on characteristics of the patient population.

For example, patients in the age range of 65 to 70 are likely

to be healthier than patients in the age range of 85 to 90.

Patients with diabetes are likely to have more care needs than patients with

no chronic diseases.

These different risks are important in many aspects of the ACO model.

Let's take a closer look at quality measurement and

then dig a little deeper in to risks stratification.

We'll begin with a big-picture look at quality.

The Affordable Care Act, or ACA, directed the US Department of Health and

Human Services to establish a national strategy for improving health care.

The National Quality Strategy, or NQS, sets six priority areas.

One, making care safer by reducing harm caused in the delivery of care.

Two, ensuring that each person and family is engaged as partners in their care.

Three, promoting effective communication and coordination of care.

Four, promoting the most effective prevention and treatment practices for

the leading causes of mortality, starting with cardiovascular disease.

Five, working with communities to promote wide

use of best practices to enable healthy living.

Six, making quality care more affordable for individuals, families, employers,

and the governments by developing and spreading new healthcare delivery models.

The priority areas are sometimes called NQS domains.

CMS's value-based payment models explicitly align quality

measurement to those domains.

Here's an example of alignment with a national quality strategy.

For 2016, the quality metrics for Medicare ACOs include 34 measures.

These measures fall into four of the six NQS domains.

One, patient and or caregiver experience.

Two, care coordination and, or safety.

Three, preventive health.

And four, care for at risk populations.

As this table reflects, CMS's expectations for

quality measurement become more demanding over time.

During an ACOs first year of participation the ACO meets the standards

by providing an accurate and complete report of quality measures.

This type of measurement is called pay for reporting.

In following years,

the ACO also will be accessed on how well it performs on certain measures.

This type of measurement is called pay for performance.

In unit five, we learned about different types of measures.

As a reminder, process measures look at whether a particular action was performed.

For example, a process measure might assess

whether a provider tested patient's blood sugar levels as recommended.

Historically, process measures have been more common,

because they could be more easily produced from the available data.

Outcomes measures, on the other hand, look at how the patient fared.

For example, an outcome measure might access whether a patients blood sugar

level was under good control during the measurement period.

Outcomes measures require more clinical data and are difficult to produce in

a reasonable time and without a labor intensive chart review.

Unless data is being captured in a EHR.

Currently, CMS is working to increase it's use of outcomes measures.

We should note that private payers who operate ACO models

may not use the same set of measures required for the CMS models.

However, in general, these private payers maintain a set of quality measures that

work in conjunction with the attributed patient population and

risk stratification factors to set pay for performance targets.

Let's take a closer look at risk stratification.

To understand the importance of risk stratification consider

that a severely ill patient is likely to require more services and

that even with excellent care.

The patient may still have worst outcomes than a patient who is relatively healthy.

If the value based arrangement fails to account for

those differences it can create an unintended consequence,

where providers have an incentive to avoid caring for higher needs patients.

Taking risk factors into account makes measurement and payment more accurate and

fair.

In quality measurement, risk stratification effects scores.

So, providers who care for sicker patients,

aren't unfairly compared with providers who care for healthier patients.

This is especially important when providers payments are linked to quality

performance or when providers performance is going to be publicly reported.

In a risk stratification approach,

patients can be divided into different Different quality measurement groups.

High-risk patients could be counted in one measure and low-risk patient in another.

Risk stratification can help identify places where there are disparities in

the care that different groups received.

A related approach is to define the quality measures so

some patients are excluded.

For example a surgical care measure may be defined so

it does not count patients who would not benefit from surgery.

In payments, risk stratification reflects the additional cost of caring for

higher risk patients.

Imagine if an ACO got paid the same amount to care for a very sick patient who was

in the physician's office every other week, as it got paid to care for

a healthy patient who just needed an annual wellness exam.

That would set up a pretty strong incentive for

the ACO to try to serve only the healthy patients.

To prevent that problem, CMS includes risk adjustments in its payment model and

it monitors the medical ACOs to make sure they aren't avoiding at-risk patients.

For reimbursement, CMS uses a model called the CMS Hierarchical Condition Category or

HCC to calculate ACO beneficiary risk scores.

The system uses diagnosis Information from claims data for

individual patients to create an individual risk score.

Which is then averaged across the assigned beneficiaries to create

a risk adjustment factor for the beneficiary group.

In addition, providers can use risk stratification data to target carers who

meet patients' needs, especially for patients who

are at high risk of complications if their care isn't carefully coordinated.

For example, a safety net provider organization in Colorado

analyzed its data to identify different levels of patient needs and

then matched services to the needs.

All patients could choose to receive text messages,

reminding them of appointments and recommendations for preventive care.

While higher risk patients also would receive complex care coordination support

with a care team that included patient navigators.

In defining different risk levels, the organization thought about the patients

currently being seen in primary care clinics and the patients who needed

services, but had not been presented at the clinics and had lacked a medical home.

To help ACOs with their data needs,

CMS pulls together information from Medicare enrollment and claims data, and

provides each ACO with aggregate information about its assigned population,

and financial performance, on a quarterly basis during the performance year.

For CMS, access to past history can be easier as CMS is in

essence a single payer of healthcare for patient populations that are 65 and older.

Private insurers maybe challenged in gaining access to

equal levels of information for their patient populations.

Now that we understand why risk stratification is important

let's look at an area where it gets difficult.

One of the challenges in risk stratification

involves social determinants health.

These are the many environmental factors that affect a person's health.

These factors have a big impact on health but

they are difficult to address in current quality measurement and reporting.

Health care systems and

quality measures generally track information about medical care.

As this diagram shows, medical care is one factor in health outcomes, but

it is not the largest factor.

Providers can control the medical care they provide to patients.

They can ask about and try to affect patient's health behaviors, for example by

counseling patients on a balanced diet or offering smoking cessation programs.

Factors such as social and societal characteristics and total ecology however,

have a greater overall effect on health outcomes and

most healthcare organizations aren't well equipped to address those factors.

A healthcare provider has very little ability to influence whether a patient

lives in a safe home, for example.

The providers EHR probably has data feeds for the patients current address, but

not for the details of the patients housing situation.

Quality measures generally don't take housing status into account.

But housing has an enormous impact on the patient's health.

In designing value based outcomes and reimbursement models, accounting for

social determinants of health is difficult.

CMS has been criticized for using quality metrics that may

unfairly penalize hospitals and providers in low income areas.

Recently, CMS announced an initiative.

The accountable health community model meant to improve health outcomes

by promoting collaboration between clinical healthcare and

community social support organizations.

In addition, the National Quality Forum, or NQF,

is examining the role of socioeconomic factors in quality measures.

For those who are interested in seeing social determinants data at a state and

county level.

The county health rankings and road maps program, which is a collaboration between

the Robert Wood Johnson Foundation and the University of

Wisconsin population health institute provides a wealth of information.

Let's look at some of the other challenges in collecting measures of cost

and quality.

You may recall from unit five that there may be challenges in data collection for

measurement.

For example, do providers consistently record information?

Is the data in the same place and consistently defined?

Some of these challenges relates to the use of EHRs.

Health information technology systems, must be designed and

implemented correctly.

Although certified EHR technology requirements include the ability

to calculate some quality measures,

the certification testing doesn't always find all the bugs in a system.

These bugs can cause issues in the logic of quality reports,

that must be corrected to meet the requirements.

And to be fair, sometimes the reporting logic of the measure

specification has errors that needs to be resolved.

In addition, if the EHR isn't implemented and

configured correctly, there could be inaccurate reports.

For example, an incorrectly configured EHR might pull data from the wrong fields,

resulting in inaccurate quality measures.

Workflow is the most common element overlooked in preparing to report on

quality outcomes.

Their provider, or other person using the EHR, might not understand the workflow

steps, and may consequently miss important clicks or documentation needed

in the electronic record, in order for the quality reporting measures to be accurate.

Sometimes a provider who has had the appropriate training may be unwilling or

unable to follow the workflow.

This may result in them not completing the required steps in the EHR, and

thus producing inaccurate quality measures.

As we've discussed, quality measurement is important.

Currently, however, it also places huge administrative burdens on providers and

their staff.

According to one survey of physician practices, practices reported that

their physicians and staff spend 15.1 hours per physician per week

dealing with external quality measures, including the following.

Tracking quality measure specifications, developing and

implementing data collection processes, entering information

into the medical record, and collecting and transmitting data.

This is equivalent to 785.2 staff and physician hours per physician per year.

The average physician spent 2.6 hours per week, enough time to care for

approximately nine additional patients dealing with quality measures.

Staff other than physicians spent 12.5 hours per physician per week

dealing with quality measures, with the largest proportion,

6.6 hours, by licensed practical nurses and medical assistants.

This happens for a number of reasons.

Different payers and oversight bodies often require different measures.

Measures may be required for different purposes,

such as quality improvement, public health goals, transparency on costs and

outcomes, regulatory requirements related to health and safety or

accreditation, payment and purchasing decisions, or other needs.

Even if each organization requires a reasonable number of measures,

the total number of measures quickly adds up.

Making the problem worse,

payers may require similar measures with slightly different specifications.

So the same quality reporting can't be reused across the payers.

Ideally, EHRs would simplify reporting

by using the data that providers are capturing as part of providing care, and

calculating quality measures without additional effort.

That is not the current state, however.

Instead, the average physician in the survey was reported to spend 2.3

hours per week entering information into the medical record only for

the purpose of reporting for quality measures from external entities.

Licensed practical nurses and medical assistants spent even more time on

this information entry, 6.1 hours per practitioner per week.

More work is needed, so

that measures can be produced from EHRs without this additional burden.

Finally, there is also a lot of dissatisfaction with many of the measures

currently in use.

Providers often are frustrated with measures that don't reflect their

particular specialty, or

that aren't perceived as accurately representing quality.

Other critics point to the lack of good measures of value,

addressing both outcomes and cost.

So while there is widespread agreement that quality measurement is important,

there is still plenty of work to be done to improve measurement itself.

In the movement toward value-based care and payments, there is

a recognition that the current challenges around reporting need to be addressed.

There is a lot of discussion about the need for metric alignment.

And in early 2016, the Core Quality Measures Collaborative,

which includes CMS, commercial health plans, physician groups, and

other stakeholders, announced a set of seven clinical measure sets that would

support multi-payer alignment of quality measures for physician quality programs.

Large healthcare organizations often work closely with their IT vendors or internal

IT staff to optimize their EHR, to address issues around quality reporting.

Smaller organizations and provider practices often don't have the staff or

financial resources to focus on that work.

However, in the Comprehensive Primary Care plus, or CPC+ program,

announced by CMS in 2016, there is a requirement for Track

2 practices participating in alternative payment models to have a letter of

commitment from their health IT vendor, that they will be partners in achieving

the requirements of the program, including risk stratification and reporting.

We have talked about resistance or

lack of training to complete designed workflows that capture the information

in the required format to generate accurate reports.

There may be different solutions for different settings.

Sometimes additional provider training is necessary.

Sometimes care team roles can be defined to maximize the efficiency of capturing

the necessary data from the patient visit.

Sometimes workflows need to be redesigned.

Many healthcare delivery organizations are also investing in health IT

solutions beyond their EHRs, including data aggregation tools.

These can help not only with the measurement and

reporting required through value-based contracts, but also with the benchmarking

and quality improvement projects that are an important element of moving the needle.

And making sure that individual providers and

clinic teams are achieving quality outcomes for

both individuals patients, and patient panels attributed or assigned to them.

This concludes Lecture a of Outcomes and Reimbursements.

In summary, this lecture provided background information about the old

HMO model, how HMOs compare with ACOs, and

how ACOs are designed to ensure quality and reduce costs.

Then we took a closer look at how quality measurement, attribution, and

risk stratification work together.

Finally, we looked at challenges in measurement and reporting.

Outcomes and Reimbursements: Foundations

HI-FIVE: Health Informatics For Innovation, Value & Enrichment (Clinical Perspective)

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