Eliminate These Five Flaws to Improve Asset Utilization in Healthcare

Posted on October 4, 2017 I Written By

The following is a guest blog post by Mohan Giridharadas, Founder and CEO, LeanTaaS.

The passage of the Health Information Technology for Economic and Clinical Health (HITECH) Act accelerated the deployment of electronic health records (EHRs) across healthcare. The overwhelming focus was to capture every patient encounter and place it into an integrated system of records. Equipped with this massive database of patient data, health systems believed they could make exponential improvements to patient experiences and outcomes.

The pace of this migration resulted in some shortcuts being taken — the consequences of which are now apparent to discerning CFOs and senior leaders. Among these shortcuts was the use of resources and capacity as the basis of scheduling patients; this concept is used by hundreds of schedulers in every health system. While simple to grasp, the definition is mathematically flawed.

Not being able to offer a new patient an appointment for at least 10 days negatively impacts the patient experience. Likewise, exceeding capacity by scheduling too many appointments results in long wait times for patients, which also negatively impacts their experience. The troubling paradox is that the very asset creating long wait times and long lead times for appointments also happens to perform at ~50 percent utilization virtually every day. The impact of a mathematically flawed foundation results in alternating between overutilization (causing long patient wait times and/or long delays in securing an appointment) and under-utilization (a waste of expensive capital and human assets).

Here are five specific flaws in the mathematical foundation of health system scheduling:

1. A medical appointment is a stochastic — not deterministic — event.

Every health system has some version of this grid — assets across the top, times of the day for each day of the week along the side — on paper, in electronic format or on a whiteboard. The assets could be specific (e.g., the GE MRI machine or virtual MRI #1, #2, etc.). As an appointment gets confirmed, the appropriate range of time on the grid gets filled in to indicate that the slot has been reserved.

Your local racquet club uses this approach to reserve tennis courts for its members. It works beautifully because the length of a court reservation is precisely known (i.e., deterministic) to be exactly one hour in duration. Imagine the chaos if club rules were changed to allow players to hold their reservation even if they arrive late (up to 30 minutes late) and play until they were tired (up to a maximum of two hours). This would make the start and end times for a specific tennis appointment random (i.e., stochastic). Having a reservation would no longer mean you would actually get on the court at your scheduled time. This happens to patients every day across many parts of a health system. The only way to address the fact that a deterministic framework was used to schedule a stochastic event is to “reserve capacity” either in the form of a time buffer (i.e., pretend that each appointment is actually longer than necessary) or as an asset buffer (i.e., hold some assets in reserve).

2. The asset cannot be scheduled in isolation; a staff member has to complete the treatment.

Every appointment needs a nurse, provider or technician to complete the treatment. These staff members are scheduled independently and have highly variable workloads throughout the day. Having an asset that is available without estimating the probability of the appropriate staff member also being available at that exact time will invariably result in delays. Imagine if the tennis court required the club pro be present for the first 10 and last 10 minutes of every tennis appointment. The grid system wouldn’t work in that case either (unless the club was willing to have one tennis pro on the staff for every tennis court).

3. It requires an estimation of probabilities.

Medical appointments have a degree of randomness — no-shows, cancellations and last-minute add-ons are a fact of life, and some appointments run longer or shorter than expected. Every other scheduling system faced with such uncertainty incorporates the mathematics of probability theory. For example, airlines routinely overbook their flights; the exact number of overbooked seats sold depends on the route, the day and the flight. They usually get it right, and the cancellations and no-shows create enough room for the standby passengers. Occasionally, they get it wrong and more passengers hold tickets than the number of seats on the airplane. This results in the familiar process of finding volunteers willing to take a later flight in exchange for some sort of compensation. Nothing in the EHR or scheduling systems used by hospitals allows for this strategic use of probability theory to improve asset utilization.

4. Start time and duration are independent variables.

Continuing with the airplane analogy: As a line of planes work their way toward the runway for departure, the controller really doesn’t care about each flight’s duration. Her job is to get each plane safely off the ground with an appropriate gap between successive takeoffs. If one 8-hour flight were to be cancelled, the controller cannot suddenly decide to squeeze in eight 1-hour flights in its place. Yet, EHRs and scheduling systems have conflated start time and appointment duration into a single variable. Managers, department leaders and schedulers have been taught that if they discover a 4-hour opening in the “appointment grid” for any specific asset, they are free to schedule any of the following combinations:

  • One 4-hour appointment
  • Two 2-hour appointments
  • One 2-hour appointment and two 1-hour appointments in any order
  • One 3-hour appointment and one 1-hour appointment in either order
  • Four 1-hour appointments

These are absolutely not equivalent choices. Each has wildly different resource-loading implications for the staff, and each choice has a different probability profile of starting or ending on time. This explains why the perfectly laid out appointment grid at the start of each day almost never materializes as planned.

5. Setting appointments is more complicated than first-come, first-served.

Schedulers typically make appointments on a first-come, first-served basis. If a patient were scheduling an infusion treatment or MRI far in advance, the patient would likely hear “the calendar is pretty open on that day — what time would you like?” What seems like a patient-friendly gesture is actually mathematically incorrect. The appointment options for each future day should be a carefully orchestrated set of slots of varying durations that will result in the flattest load profile possible. In fact, blindly honoring patient appointment requests just “kicks the can down the road”; the scheduler has merely swapped the inconvenience of appointment time negotiation for excessive patient delays on the day of treatment. Instead, the scheduler should steer the patient to one of the recommended appointment slots based on the duration for that patient’s specific treatment.

In the mid-1980s, Sun Microsystems famously proclaimed that the “network is the computer.” The internet and cloud computing were not yet a thing, so most people could not grasp the concept of computers needing to be interconnected and that the computation would take place in the network and not on the workstation. In healthcare scheduling, “the duration is the resource” — the number of slots of a specific duration must be counted and allocated judiciously at various points throughout the day. Providers should carefully forecast the volume and the duration mix of patients they expect to serve for every asset on every day of the week. With that knowledge the provider will know, for example, that on Mondays, we need 10 1-hour treatments, 15 2-hour treatments and so on. Schedulers could then strategically decide to space appointments throughout the day (or cluster them in the morning or afternoon) by offering up two 1-hour slots at 7:10 a.m., one 1-hour slot at 7:40 a.m., etc. The allocation pattern matches the availability of the staff and the underlying asset to deliver the most level-loaded schedule for each day. In this construct, the duration is the resource being offered up to patients one at a time with the staff and asset availability as mathematical constraints to the equation (along with dozens of other operational constraints).

Health systems need to re-evaluate the mathematical foundation used to guide their day-to-day operations — and upon which the quality of the patient experience relies. All the macro forces in healthcare (more patients, older patients, higher incidence of chronic illnesses, lower reimbursements, push toward value-based care, tighter operating and capital budgets) indicate an urgent need to be able to do more with existing assets without upsetting patient flow. A strong mathematical foundation will enable a level of operational excellence to help health systems increase their effective capacity for treating more patients while simultaneously improving the overall flow and reducing the wait time.

About Mohan Giridharadas
Mohan Giridharadas is an accomplished expert in lean methodologies. During his 18-year career at McKinsey & Company (where he was a senior partner/director for six years), he co-created the lean service operations practice and ran the North American lean manufacturing and service operations practices and the Asia-Pacific operations practice. He has helped numerous Fortune 500 companies drive operational efficiency with lean practices. As founder and CEO of LeanTaaS, a Silicon Valley-based innovator of cloud-based solutions to healthcare’s biggest challenges, Mohan works closely with dozens of leading healthcare institutions including Stanford Health Care, UCHealth, NewYork-Presbyterian, Cleveland Clinic, MD Anderson and more. Mohan holds a B.Tech from IIT Bombay, MS in Computer Science from Georgia Institute of Technology and an MBA from Stanford GSB. He is on the faculty of Continuing Education at Stanford University and UC Berkeley Haas School of Business and has been named by Becker’s Hospital Review as one of the top entrepreneurs innovating in healthcare. For more information on LeanTaaS, please visit http://www.leantaas.com and follow the company on Twitter @LeanTaaS, Facebook at https://www.facebook.com/LeanTaaS and LinkedIn at https://www.linkedin.com/company/leantaas.