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19 Articles in Volume 20, Issue #4
20/20 with Dr. Nathaniel Katz: Pain Research and Future Therapeutics
A 20-Year Timeline: Pain Therapeutics and Regulations
A Comparison of the Alpha-2-Adrenergic Receptor Agonists for Managing Opioid Withdrawal
A Pain Assessment Primer
After the Task Force: A Conversation with Vanila A. Singh, MD
Ask the PharmD: Can opioids and benzodiazepines ever be used together?
Cognitive Strategies and Mindful Awareness for Integrative Pain Care
COVID: Clinical Considerations for Acute and Post-Infection Symptoms
Editorial: Fudin and Gudin Tackle Pain Care History – Asking, Have We Done a 180?
From Hands-On to Home-Based Care: Physical Therapy Undergoes a Paradigm Shift Due to Pandemic
MS-Related Pain and Spasticity: Are Cannabinoids an Option?
New Biological Agents for Psoriatic Arthritis: A Monoclonal Antibody Primer
Pandemic Presents Unexpected Opportunity to Embrace Multimodal Analgesia and the Integrative Care Team
Provider Perspective on Knee OA: Injections and RFA Options
Redefining the “Pain Specialist” of Today
Resident’s Corner: Climbing the Learning Curve in Pain Management
The Evolution of Pain Management: Experts Weigh In
Tips from the Field: How to Enhance Practice Efficiency
Tumor Necrosis Factor (TNF) Inhibitors: A Clinical Primer

Tips from the Field: How to Enhance Practice Efficiency

How outpatient and academic medical centers can use process engineering to effectively manage bottlenecks, resources, and late patients – and still deliver value-based care.

Medical practices are facing growing pressure to provide value-based care, which is defined by a high ratio of positive outcomes to total cost of care.Increasing this ratio requires that the right patient see the right provider at the right location for the optimum amount of time at optimum cost.

Operationalizing this idea requires the use of effective process engineering to build scalable practices to deliver these features. In this article, we describe the experience of a multidisciplinary team of physicians and business operations management faculty who collaborated across the medical and business schools at one university to work toward these goals.


Practice Efficiency in Academic and Private Practices

The approach to process engineering that we have developed was first used in a pain clinic at an academic medical center (AMC). The distinction between this setting and private practice (PP) is meaningful because the difference in missions creates differences in process flows. While both the PP and AMC provide care within the practitioners’ community, the AMC has an expanded role to provide tertiary/quaternary care, educate future providers, and generate new knowledge.

For both the AMC and PP, the goal of an ambulatory practice ­– the site of service for most pain clinics – is to maximize the time that providers spend face to face with patients (“face time”) within the larger goal of increasing value-based care.

Health outcomes are clearly a function of access to, time with, and behavior of the providers. Thus, the provider is a major driver of service value. At the same time, the provider is likely to be the most expensive hourly resource in the care delivery process, particularly in outpatient settings.

Clinical productivity is traditionally defined as the number of patients seen per hour by each clinical provider, while financial productivity is defined as the difference between revenue generated by each clinical provider and cost of care for each patient seen. Consequently, the utilization of the provider is a key driver of system cost. Thus, the delivery of value-based care (benefit:cost) hinges on the provider to influence both components of that metric.


Determinants of Operational Efficiency

Calculation of operational efficiency invariably depends on knowing specific activity times and durations. These values include:

  • appointment times
  • arrival times
  • activity (task) times
  • patient wait times
  • patient flow times
  • and total session times.

Activity times are the durations required to perform the various tasks involved in the patient care process. It is crucial to measure actual activity times throughout the practice. Simply using standard times or billing standards will not work because each practice has nuances that drive deviations from these values, and management of these nuances is the key to incremental improvement.

For example, most published works about running a clinic assume that patients arrive on time; most guidance on practice management assumes that all patient waiting takes place in a waiting room, and virtually all academic work on clinic flow assumes that processing rates are independent of the system state. Each of these notions is patently false.

Our work repeatedly finds that unpunctuality is an issue for both patients and providers.Furthermore, detailed observations show that waiting times in examination rooms often exceed times in waiting rooms,3,4 and that processing rates often decrease when providers sense that they are falling behind schedule.5

Factors such as these imply that management of clinic efficiency is much more complex than typically thought. Therefore, it is necessary to have an organized approach to data collection, analysis, and improvement. 

The authors have has developed a methodology to approach process improvement efforts in a pain clinic setting that can be adapted for ambulatory surgical centers and imaging centers. (Image: iStock)

Our Model for Process Improvement

Over the past decade, our group has developed a methodology to approach process improvement efforts in a clinic setting.Note that this model was applied within an outpatient/ambulatory pain medicine practice, but it can be adapted for use in other settings such as ambulatory surgical centers and imaging centers. This approach involves a process to map, measure, and evaluate efficiency and identify changes that help to manage bottlenecks and resource utilization issues.

The process includes six steps:

  1. Describe the process that delivers care in a relevant way.
  2. Collect data on activity times and workflow times.
  3. Simulate the systems under study by creating a virtual operating model.
  4. Experiment with both the virtual and real systems to identify and test possible changes in the metrics of interest.
  5. Propose process changes to enhance performance in metrics of interest.

6. Predict changes in the metrics that stem from the implemented process change outlined above.


The original impetus for developing this process came from the need to merge a community-based PP clinic with the practice in our AMC. This consolidation introduced several logistical issues that we could restate as research questions, such as:

  1. How does adding a teaching element to a merged private practice/academic medical center affect patient throughput and patient wait times?
  2. Could the educational process be reorganized to improve both the educational experience and clinic performance?
  3. How can patient punctuality be improved to enhance clinic efficiency?
  4. How can we understand the impact of clinic congestion and patient punctuality on physician processing times? 

These questions focused on the key determinants of practice efficiency and helped us to examine effects and possible solutions. They are revealed in the process of applying our framework. Here is what we found.

There are gaps in the care delivery process between private practice and academic medical centers

A private practice pain clinic was merged into an academic setting. Fortunately, before the merger, we had developed a process flow map for the PP (Figure 1) and collected arrival and activity time data for approximately 18 months. Once a decision was made to move the practice to the academic medical center, our team decided to develop a detailed map of the flows in the AMC (Figure 2) and to conduct a detailed study of activity times in this setting.

Given mapping and data collection in both settings, the merger of practices created an opportunity to conduct a natural experiment wherein we could compare the performance of the two clinics under similar resource loads.However, this comparison introduced a significant problem. The teaching mission of the AMC requires a different process flow and has a different appointment schedule. To control for these factors, we created discrete event simulations of both settings using the relevant data. We were then able to document and explain why the AMC could sustain greater throughput and reduced cycle times than the PP, despite having more process steps.

The key to this outcome was the use of parallel processing, which refers to having one resource act on one patient while a different resource simultaneously acts on another. Modeling further exposed that a key driver of waiting times in the AMC was management of the teaching function within the care process, leading our team to address the second question.



Arranging the educational process improves clinic performance

Simulation models enabled us to show that a 1-minute reduction in teaching time for selected cases could result in a 3-minute reduction in wait time for all patients. This amplification of time saved occurs because interaction between an attending and a resident while the patient waits in the examination room occupies three critical resources simultaneously: attending, resident, and room. Consequently, streamlining this process has a disproportionate effect on waiting times.

We then conducted a pilot study in which we instituted “pre-processing” of the didactic educational component of the attending-resident interaction using a checklist.8We found that by pre-processing the non-clinical review/planning of the resident education, we were able to reduce wait times on the average by 14 minutes per patient, throughput times by 13 minutes, and session times by 28 minutes.


Some private practice and academic medical centers use a Real-Time Location System (RTLS), which utilizes infrared technology to track and time examination room entrances and exits. (Image: iStock)

Patient punctuality and clinic congestion can be improved with policy

Before the merger of our PP clinic with the AMC, our patient satisfaction survey revealed that some patients found the wait time too long even though our clinic typically ran on time. Part of the problem was that a small number of patients arrived late, and the resulting delay rippled through the system.

We instituted a “no-lateness policy” with three components:

  • adding a statement to the appointment letter that tardy patients would be asked to reschedule
  • emphasizing the policy that patients are contacted by telephone 24 to 48 hours in advance
  • posting a notice at the sign-in desk as a visual reminder.2

Within 6 months, the number of late patients had declined by over 50%, and after 1 year, fewer than 2% of patients were late by an average of less than 3 minutes. Additionally, we improved our “run-on time” rate to 97%.  

Next, we conducted a retrospective examination of data from three clinics of varying complexity and size to determine the effect of patient punctuality and clinic congestion on physician processing times. The novel question addressed here was, “how do the physician’s activity times change if the system is running behind schedule?”5

To understand our findings, imagine dividing patients into three groups.

  • Group A consists of patients who arrive on time and are seen on time
  • Group B patients arrive on time but are seen late
  • Group C patients arrive late.

We found that Group A patients experienced more time with the provider than did patients in Groups B or C. In other words, “face time” dropped when the clinic fell behind schedule. This finding was intuitively obvious but rarely documented. Again, while the impact of behavior is subtle, ignoring it may lead to misleading interpretations of other results.


Use Technology to Enhance Patient Throughput

One challenge to developing a complete understanding of patient flows in outpatient settings is the absence of a mechanism to track movements through the system in real time. Getting – maintaining – accurate timestamps for all movement in these settings is surprisingly difficult, and much of the data routinely recorded is cluttered with inaccurate entries.

Some organizations use a Real-Time Location System (RTLS), which utilizes infrared technology to track and time examination room entrances and exits. We were fortunate to be able to use such a system; we triangulated the data with the electronic medical records and a patient outcomes database in a large radiation oncology clinic. One advantage of this approach is that it allowed us to measure wait times in examination rooms. We found that these times often exceeded time spent in waiting rooms.

This study also led to the creation of a novel metric that we named “face-time efficiency.”3,4  Using the RTLS within our improvement process, we found that face-time efficiency was driven down by the frequent interruptions that attending physicians experience. Using discrete event simulations built around the collected data, we were able to isolate the effects of these interruptions and show that face-time efficiency can be improved significantly if these interruptions are managed.


Addendum: Medical Practice in a Pandemic and Post-Pandemic World

Adding steps, such as social distancing, real-time testing, and higher standards for cleaning − without adding resources or increasing payments − means efficiency of medical operations will be central to the survivability of many practices.

Over the past 12 years, our Medical Operations Research Core team has worked with faculty, students, residents, and fellows to complete over 25 health process improvement projects. While the focus of these projects was on improving service quality and resource utilization, we also kept track of the potential impact of this work on the bottom line.

Our recommendations would typically include ways to adjust process flow and scheduling templates to enable the addition of 1 patient to a 10 to 15 patient schedule for a 4-hour clinic session. This increases revenue by 5% to 10% without increasing resource levels.

The COVID-19 pandemic shifts the discussion. In a post-pandemic world, the needs for social distancing, real-time testing to limit exposure to infections, and higher standards for cleaning spaces such as exam rooms will add operating costs, increase the number of steps in care delivery processes, and add constraints on the turn-around times for key resources. This will require new paradigms to manage clinic schedules that will require careful tuning of patient arrivals and their face time with providers. Since this is not likely to be matched with increases in reimbursement rates, the role of medical operations management as a strategic tool will be more prominent than ever.

The challenge of adding steps to processes without adding resources or increasing payments means that improving the efficiency of medical operations will be central to the survivability of many practices, clinics, and hospitals.

Last updated on: August 3, 2020
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