John Marchisin

home

authors

john-marchisin

Health systems and hospitals often lack the capacity to align clinical schedules with the correct time estimates for many diagnoses and procedures. More specifically, provider practices in both inpatient and outpatient settings are usually based on crude schedules that assign generic time slots based on patient category-for example, new visit versus follow-up-or diagnosis. Even sophisticated systems used for electronic health records, scheduling, and billing don’t have the capacity to fully optimize physicians’ and advanced practice professionals’ schedules.

Inaccurate forecasting of the time needed with patients can result in many negative knock-on effects, starting with a basic inability to accommodate for individual patient variability. While the most obvious issue here might be inadequate time to complete individual patient encounters, there can also be empty time within individual patient encounters.
	
	While the patients themselves experience frustrating and unpredictable service delays and billing inequities as a result, health systems, physicians, and provider practices are all impacted. Inefficient scheduling not only affects the patient experience, but operationally, providers suffer lost volume and revenue through these mismatches in productivity and reimbursement. Moreover, it's nearly impossible to accurately analyze and compare provider performance and cultivate “best patient encounter practice” among a group of peers.

This is one area of healthcare where a data-based approach can make a world of difference. With a dynamic scheduling solution in place, using granular demographic, diagnostic, geographic (facility or office location), and provider data, health systems and medical practice groups can consistently and capably accommodate patient needs, capture the revenue they deserve, drive provider efficiency, and harness data to engage in performance improvement.

Understanding key healthcare concerns stemming from ineffective scheduling


	Along with lost revenue and negative patient experience, other serious problems often emerge from retaining an antiquated approach to scheduling and workflow, including:

: Constant pressure to see more patients, produce more relative value units (RVUs), and generate more revenue is a major source of burnout among providers. This pressure is magnified when physicians deliver care that is captured inaccurately or not documented in coordination with the encounter time, or when they perform services that could otherwise be capably handled by other staff-such as trained nurses, behavioral health technicians, diabetes counselors and care managers-operating at “top-of-license.”

: A lack of accurate information about the time needed to complete a specific diagnosis or procedure, as well as accounting for follow up and additional testing, makes it more difficult for a health system to gain internal insight and improve efficiency over time.

: Health systems leave time that could be used to diagnose and treat additional patients on the table, because most scheduling software averages the amount of time needed and assigns arbitrary slot lengths instead of making a more specific, data-informed time commitment. Patient access is reduced, providers are confronted with unproductive time, hospital and office flow is jeopardized, and daily schedules are extended.

Facility inadequacy and unnecessary space

: With improved scheduling comes increased patient turnover, with both better room utilization and facility management.  Not only office and inpatient unit flow can see benefit, but services such as housekeeping and patient transport can be optimized, while overall space requirements can be reduced.

A perverse disconnect between time spent and reimbursement

: When billing patient visits on codes with a recommended time-based component (including elements of counseling and coordination of care, prolonged services), reimbursement is often based on significant increments of time that may be measured in as short as 15 minutes and longer than 45 minutes. Without going into detail given the complexity of coding, this creates incentives for providers to spend extra time where it’s not necessarily mandatory.  It also creates situations where scheduled time slots may be longer than the code being billed, essentially imposing circumstances where the provider is delivering under-reimbursed care.

Why the move to dynamic, efficient scheduling makes a substantial impact


	Many health systems administrators and executives, as well as providers, already recognize that a significant issue exists with inefficient scheduling. The solution involves a number of new tactics for scheduling that improve accuracy, align schedules with more accurate projections of time spent with each individual patient, and distribute workloads.

Using demographic information, patient histories, comorbidities and other available sources of data, health system administrators can develop a more accurate predictive scheduling process that takes into account the unique needs of a patient, whether more or less time is forecasted. The predictive tools used as part of this approach can dynamically adjust schedules, allowing for continued improvement and accuracy as time goes on. Robust comparisons between different providers and offices can also be generated, allowing for insight into scheduling that can lead to additional positive adjustments and a greater understanding of the process.

This leads to more effective use of all professionals’ time and skills within a health system or specific office. With a deeper grasp of the individual needs of patients, health systems can utilize high-level providers like physicians in areas where only they can provide care, while nurses, technicians, counselors and other medical staff can be tapped to provide appropriate care and accurately capture and bill all interactions. Scheduling these interactions with other professionals allows physicians to keep their schedules on track. This is especially important in the context of providing access to in-demand primary care providers and specialists, reducing wait time, and increasing the number of patients they can diagnose and treat. With doctors having so little free time throughout the day, this benefit can easily prove to be one of the most important ones realized with the move to a more dynamic approach to scheduling.

A few easily avoided pitfalls that health system leaders must keep in mind


	Implemented correctly and managed competently, this scheduling strategy aligns resources with actual need, without reduction in quality of care or a negative impact on the patient experience. Health system administrators and leaders must remain aware of the potential for these consequences to occur. Scheduling should never be used, for example, as a weapon to force some providers to work more quickly, which can lead to burnout as well as the very issues with patient experience and care quality that this process is designed to address. Providers need to understand the reasons behind such a system being implemented as well as the implementation itself-including that it takes a more humane approach to scheduling valuable providers’ time, as much as optimizing patient volumes, flow, and revenue. Otherwise they may fear that a push to see more patients is the only reason for making the change.

Value-based care makes dynamic scheduling even more important


	In the world of value-based care, where quality and patient management will dictate profitability, improved scheduling has clear value. The continuing shift to the value-based care model means adopting dynamic scheduling that provides deep insight into individual patients and sets appointments better aligned to care needs will only become more valuable as time goes on. Because it intelligently uses many data sources to arrive at a more specific and accurate allocation of resources, the dynamic approach to scheduling can allow health system administrators to better understand the cost of care associated with each patient, as well as collectively for the entire population. It puts health systems in an exceptionally strong position to manage financial and outcome pressures, allowing them to adapt to industry changes and benefit now and far into the future.

John Marchisin is managing director and Steven Nemerson, MD, is a subject matter expert in the healthcare practice of AArete, a global consultancy specializing in data-informed performance improvement.

Articles

Inaccurate forecasting of the time needed with patients can result in many negative effects, starting with a basic inability to accommodate for individual patient variability. 

Dynamic Schedule Process Benefits Hospitals, Providers, and Patients

Hospital-Acquired Conditions (HACs) continue to burden our healthcare system and patient health. These events arise during medical stays and are often avoidable complications of care – including pressure ulcers, air embolisms, falls and trauma, and surgical infections following certain procedures.

, HACs increased the average length of hospital stays by eight days in 2016, raised mortality risk by 72%, and accounted for more than $2 billion in excess hospital costs. Hospitals and payers could take a much more proactive approach to avoiding these conditions by applying evidence-based guidelines and using data to identify opportunities to create sustained avoidance of HACs.

Hospital acquired conditions have had such a huge impact on the U.S. health system that payers and providers have no choice but to prioritize addressing the situation.

CMS has rightly started to take notice. In fiscal year 2015, CMS mandated the 

Hospital-Acquired Condition (HAC) Reduction Program

, reducing payments by 1% for hospitals that rank among the lowest-performing 25% with regard to HACs. CMS has also included specific measures related to HACs in its other Hospital Quality programs, such as the Inpatient Quality Reporting (IQR) Program and its Value-Based Purchasing (VBP) Program.

The Impact of Big Data on Medical Decisions

Noncompliant hospitals therefore lose twice-with poorer standards and therefore reputational risk, and also from regulatory fines.

What can payers and providers do to address the issue and improve their numbers while improving health outcomes?

With increased emphasis on reducing HACs and associated medical costs, one approach many healthcare payers and providers are taking involves the use of predictive analytics, which can shed light on current conditions and also suggest future actions to take to reduce the HAC incidents.

There are big savings, as well, given that providers are responsible for the cost of treatment including extended inpatient stays under pay for performance and value based arrangements. Using data analytics to report more accurately on these incidents has been shown to reduce HAC spend by as much as 15 to 30%.  

What are some of the challenges inherent in taking this approach?

We’ve found that both payers and providers have trouble identifying episodes of care that are related to HACs. Using the correct data, and using data correctly, can be difficult to do when it comes to determining the specific cause of an episode and whether it was driven by a patient’s health status or by insufficient care at the facility.

There’s also a great deal of manual input that’s necessary to form a workable strategy for HAC surveillance-and with manual input comes human error. Making the switch from subjective standards to more reliable automated or digitized standards leads to inevitable disconnects between what the data show and what is happening on the hospital floor.

Gaps in knowledge of what causes these conditions, and determining effective prevention strategies, can create very human barriers to implementing a data analytics approach. 

Lastly, tying an episode of care related to HACs to value based care objectives requires not only a deep understanding of the data, but subject matter expertise to understand future contracts as it pertains to the data.

The nuts and bolts of a data-driven approach

As digital care comes more and more to the forefront, some of these difficulties can be overcome by employing machine learning or artificial intelligence (AI) along with predictive analytics. Using “smart” machines with access to much more data than any person’s memory could store can help in generating insights for patient safety teams, allowing providers to become more proactive and action-oriented in their approach to HACs.

By identifying common threads among HACs, and by determining risk factors through data analysis, providers can intervene much earlier and develop effective strategies to reduce adverse events. To give one example, by linking EHR data, bed alarm data, and nurse call light data to analytics, patient teams can much more accurately predict when a patient is at risk of an imminent fall.

Providers and health plans using dashboards with drill-through capabilities find it much easier to present their data analytics findings in a way that all users are able to interpret. This is where measurable savings can come in-as mentioned, these analytical reports can help healthcare organizations reduce avoidable HAC spend by 15 to 30% on average.

More important to the health of our populace, this use of data analytics produces insights that can help to improve HAC-related policies, support the development of long-term prevention strategies, and ultimately improve health outcomes.

Michael Kim is a VP at AArete, a global consultancy specializing in data-informed performance improvement, and heads its Center of Data Excellence.  He can be reached at 

. John Marchisin is a Managing Director at AArete and can be reached at 

HACs continue to burden the healthcare system.

John Marchisin

Articles

Dynamic Schedule Process Benefits Hospitals, Providers, and Patients

Can Data Analytics Lower Hospital-Acquired Condition Incidence?