Tuesday, November 11, 2014

3 Reasons Why Predictive Patient Scheduling is the Key to Primary Care Efficiency

In the 30 years I've been in healthcare there has been little innovation to optimize provider schedules and patient flow.  Patients are still frustrated by long waits and provider organizations are grappling with capacity constraint issues and rising operating costs. The most significant and often overlooked cause of patient flow inefficiency is the prevailing practice for scheduling appointments.  Visit durations are not based on data of actual time needed with the patient, and existing scheduling systems enable schedulers to enter their own interpretation of the visit reason.  This practice results in inaccurate scheduling, and causes uneven flow throughout the day.  Below are three reasons why predictive patient scheduling is the key to improving primary care efficiency.   

The Problem – unruly scheduling practice
Practice management systems and on-line scheduling services are designed to match a patient with an open appointment slot.  They are not designed to ensure that the right amount of time is given for each appointment.   They enable only broadly defined categories of appointment durations (e.g., allocating 30 minutes for new patients, and 15 minutes for established patients).  Most clinics establish written guidelines to further define visit categories and help schedulers determine the amount of time for each visit. 

There are several problems with written scheduling guidelines.  First, they are often incomplete.  Limited to one page per clinic or provider, they call out only a fraction of the reasons for primary care visits. Second, the visit durations are based on a “best guess” rather than an actual measure of provider time spent in each visit.   Third, it’s a manual process.  Without good data, updating guidelines is time-consuming and often the subject of much debate among providers. 

In practice, the guidelines are often not followed because they are difficult to access, out of date, or usurped by (unwritten) provider preferences.  Existing systems enable any scheduler to enter any visit reason and any duration, yielding significant variation.  On-line scheduling services are more rigid, requiring patients to select from a short generalized list of visit reasons.  Visits are often assigned the same or the longest appointment duration, requiring clinic staff to adjust the duration after the fact.  Regardless of how appointments are scheduled, today’s scheduling practices are unruly, and inevitably result in variation in provider schedules and inefficiency in patient flow.  

Optimizing flow – the case for predictive patient scheduling
Not many industries can afford to operate their core business with such a random process.   In today’s environment - with rising costs, provider shortages, and consumerization – it’s no longer affordable for provider organizations either.  To succeed, they need to optimize patient flow.  In much the same way as a shipping company packs a container to optimize space, providers need accurate scheduling to optimize patient flow.  This starts with predictive patient scheduling, a measured understanding of visit durations based on the actual time providers spend with patients (including time for documentation).  This data-driven rules-based scheduling approach is established by: 
  1. measuring provider time for each visit reason,
  2. analyzing the data patterns, and  
  3. incorporating evidenced-based rules into the scheduling process.  
Here’s an example:  The average primary care visit for an established diabetic patient takes 15 minutes.  With deeper analysis, the data shows a bi-modal distribution pattern – stable diabetic visits average 10 minutes, and unstable diabetic visits average 20 minutes.  Whether the patient schedules their appointment on the phone or on line, their status (stable vs. unstable) is ascertained at the time the appointment is scheduled.

Predictive patient scheduling helps:  1) improve patient experience, 2) reduce capacity constraints, and 3) reduce costs.  Here’s how:

Improve patient experience
Scheduling the right amount of time for each patient (e.g., 10 or 20 minutes vs. an average of 15 minutes in the diabetes example above) reduces the peaks and troughs that occur when back-to-back patients are scheduled with too little or too much time.  For example, if 5 consecutive patients need 5 more minutes than scheduled, the care team will be 25 minutes late starting their sixth visit.  Scheduling accurately streamlines daily flow, reduces wait time and improves the patient experience.

Reduce capacity constraints
With unpredictable flow, providers often buffer appointment durations to run on time.  Scheduling visit durations accurately reduces this “hidden access” and enables schedulers to see more appointment opportunities.  By reducing hidden access just 0.3 visits per hour (e.g., moving from 2.5 to 2.8 patients/ hour), for example, a full time primary care physician can see 2 more patients per day (e.g., moving from 20 to 22 patients/day).  For a health system with 150-physicians, the cumulative effect is significant.  It would open 60,000 more visits per year and help alleviate capacity constraints felt by most health systems today.

Reduce costs
Scheduling accurately and optimizing flow, reduces operating costs.  With a more predictable flow, there is less pressure to staff for peaks and less need to pay for overtime.  Reducing hidden access and improving throughput reduces the cost per visit.  In the example above, 60,000 more visits accommodated by existing providers is the equivalent of adding 16 providers – without the added cost! 

In the absence of intelligent visit data, individual providers or clinic sites solve their scheduling and flow problems individually.  This duplicative process is not only expensive, but also results in variable resource use (e.g., number of rooms or staff per provider) and variable outcomes (e.g., access, productivity, patient experience) across the network.  With comparable visit data, health systems can identify best practices and implement standard work to reduce costly variation.

Conclusion
The prevailing practice for scheduling appointments, whether via phone or on line, produces uneven flow, constrained access and higher costs.  Predictive patient scheduling is the key to optimizing patient flow and the mainspring to primary care efficiency.  It improves the patient and provider experience, reduce capacity constraints and reduce the cost to operate.

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