How a connected care record gets patients to the right care setting
There are always a number of factors involved in deciding when a patient is ready to be discharged from the hospital. Under the current fee-for-service payment model, declining reimbursements have incentivized hospitals to reduce their length of stay and move patients to a lower setting of care sooner. This has resulted in shifting some of the cost to post-acute care without necessarily reducing overall cost or improving patient outcomes. Discharge decisions can’t just be about finding the lowest cost setting of care, they need to be about finding the care setting that will produce the best outcomes, at the lowest possible cost, while making sure the patient has a positive experience. Patients tend to do better when they are in lower intensity settings of care. For example, sending a patient home reduces the risk of exposure to infection. The patient is also more comfortable and has more control, which helps speed recovery.
Not surprisingly, historical practice patterns often didn’t use a data-driven approach to determine the optimal setting of care for a patient after an inpatient stay. Instead, factors such as relationships, contracts, and previous referral patterns dictated where an acute care hospital would guide its patients upon discharge. Both literature and experience have revealed that there are key areas needed to succeed as hospitals shift to new value-based payment programs, they are as follows:
- data and analytics to drive decisions;
- clinical transformation to change practice patterns and behaviors of the care team, including physicians;
- management of the sub-acute network; and
- patient education and engagement.
Attacking the problem
Managing cost while guiding patients to the appropriate post-discharge setting of care requires change. According to a study published by Health Affairs, the largest variation in surgical spending is associated with the choice of post-acute care setting; home health care, outpatient rehabilitation, skilled nursing facility, or inpatient rehabilitation facility. The study further noted that health systems seeking to improve surgical episode efficiency should collaborate with patients to choose the highest-value post-acute care setting. However, guiding patients to the optimal care setting requires data, practice transformation, and patient engagement.
Armed with data that historically was hard to access and analyze, hospitals have started to scrutinize their discharge patterns. They’ve discovered that care delivered in post-acute settings has varied in both outcomes and cost, and often patients were not directed to the lowest intensity setting that would still yield positive outcomes. Using that knowledge, many hospitals have started programs to change that pattern by taking the following steps:
- Negotiating with high-performing, post-acute providers, putting those providers at risk for cost and quality. Knowing that a high-performing network is an option makes the right choice easier for the patient.
- Providing data to their providers to illuminate sub-optimal referral patterns. This helps establish a benchmark compared to higher performing physicians. With monthly monitoring in place, most providers used the data to make adjustments to their discharge plans.
- Reviewing the profiles of their patients to understand the optimal setting of care for both pre-and post-surgery.
Many hospitals now use data to improve their clinical processes for surgical patients. For scheduled surgeries, more hospital care teams are looking at the profiles of their patients before the admission. With access to comprehensive data about the patient, including problem lists, diagnoses, medications, insurance, and other key inputs such as social determinants, the clinical team is able to use predictive analytics to pre-determine which patients might be candidates for outpatient surgery or to be discharged after an inpatient stay. Those patients then begin their pre-admission planning to make the post-acute transition easier. Online tools provided through a patient portal or a mobile application also help the patient prepare and stay on track during the recovery process.
For patients with multiple chronic conditions, high acuity, or limited access to post-surgical assistance, care managers work with the patient and their family to develop a plan for the best setting of care upon discharge. Preparing those patients and planning for a stable discharge requires monitoring during their stay, educating the patient, and coordinating with the next setting of care. Since these patients tend to have a higher incidence of readmission and thus even higher cost, close monitoring both pre- and post-discharge becomes imperative. Having access to a consolidated health record, which aggregates data from all setting of care, becomes an important tool for all those involved in the patient’s journey.
Hospitals are still learning how to navigate in the new world of value-based payments. However, what they know is that using data to support the changes needed to succeed is essential. The fundamental game changer has been access to a patient’s consolidated health record. Understanding all of the patient’s information, which includes clinical, claims, administrative, social and economic data, is needed to drive change. In the next article, my colleague Dr. Russ Leftwich describes how to get access to that data, and how to make data meaningful to those involved during the care transitions.
FHIR’s role in supporting risk-based care models
My colleague also contributed a piece in this edition highlighting how connected care can help get patients to the right care setting to support risk-based care models. Building off this, the next step is understanding how this can be made possible through today’s technologies.
To be successful in the risk-based environment, healthcare entities need effective communication and collaboration between key players, from payers to clinicians to patients. Just as a bridge requires the right foundation to effectively connect physical communities, healthcare entities need the right foundation to effectively connect people, organizations, and systems.
A key part to this is ensuring that those involved in clinical decisions have access to the most insightful and actionable data sets across different sources in real-time, so care facilities can ensure that delivery is optimized throughout the patient’s journey. By having access to this information, patient risk is proactively managed, members are informed and providers are supported in their care delivery tactics. This can be achieved through an accessible, single view of data from a clinically integrated network, all while using industry-accepted messaging standards.
That, however, is where the problem arises. To be able to achieve effective data sharing and interoperability, healthcare IT systems need to be using the same version of the same value set, as well as the same “information model.” Information models can be thought of as the way you put things together to describe a data concept or definition. This is what the challenge has been for the healthcare industry for the last 30 years. Whereas information sharing used to be a lot simpler, the current IT landscape presents a number of hurdles.
Thirty years ago, if a hospital could exchange data between two IT systems, you had interoperability. But in the three decades since, the amount of data and the number of data sources has increased exponentially. Think of it this way; the human mind can only handle five pieces of data in a single decision. In 1980, about 10 pieces of data informed each complex clinical decision. Today, there are more than 500 pieces of information that go into a complex clinical decision. Interoperability must enable data access across a multitude of systems, and present that information to all healthcare entities—from clinicians to patients to payers—in a way that is digestible, understandable, usable, and actionable. With this increase in data sources and volumes, actionable has come to mean having data in an interoperable format that can be consumed by Clinical Decision Support (CDS) systems. Data represented as FHIR is well suited for this.
The good news is that a new foundation for the future of interoperability is making its way to the forefront. HL7’s Fast Healthcare Interoperability Resources (FHIR) standard has the ability to provide vastly simplified, accelerated and effective clinical information sharing between systems. This is creating opportunities for tremendous innovations in the healthcare IT industry, and in this case, is helping to support risk-based care models.
The inspiration for FHIR came from the desire to see all data from a single point in real time, similar to how it’s done in other industries. For instance, the way e-commerce, Google and social media works, where data is connected across the entire internet network from one place, and users are easily able to see all of the data that they are interested in. That’s the technology that FHIR is based on.
FHIR is, or should be, the preferred standard—particularly because it involves accessing data across many systems. This is something that hospitals couldn’t do previously, and is especially important if the focus is on mitigating risk. Today’s healthcare entities must make critical decisions about technology and solutions that can aggregate data from a variety of systems and standards, and enable the type of communication and collaboration needed in supporting risk-based models. Thankfully, because many systems are capable of sharing information, the focus should now be on the systems that have an HIE platform, which has the ability to translate and transform data from older standards to and from a FHIR representation of that data. This type of platforms will truly bring us one step closer to universal interoperability.
At the end of the day, care quality and value improve when risk is proactively managed, members are informed and engaged and providers are supported in their care delivery. Then, and only then, will hospitals be set up for success in risk-based contracts.
