Face Time Versus Test Ordering: Is There a Trade-off?
Published Online: November 25, 2013
James E. Stahl, MD, CM, MPH; Mark A. Drew, BID; Jeffrey Weilburg, MD; Chris Sistrom, MD, MPH, PhD; and Alexa B. Kimball, MD, MPH
The time patients and clinicians spend together is usually when the most important clinical decision making occurs, is central to the clinician-patient relationship, and is at the core of clinical care.1-16 Time, unfortunately, is not an infinite resource. In clinical scheduling, time is often allocated using schedule templates with blocks of time, such as 15/30 minutes or 20/40 minutes. In these limited intervals, the physician and patient must address a wide variety of concerns and complaints. This set of complaints and concerns (ie, the chief complaint, secondary, tertiary, and others) take time to address. Recent work suggests that within an encounter a clinician typically deals with one of these problems every 1 to 3 minutes.17 In this way, the clinical encounter might be considered a packing problem. A packing problem is a class of optimization problems where the object is to pack objects together as densely as possible18 (also often referred to as knapsack or pallet problems). In the case of the clinical encounter, a packing problem refers to how much useful activity (eg, addressing clinical complaints) a clinician and patient can “pack” into their limited time together.
If time is limited, how do clinicians respond? As encounter time shrinks, as it may do under volume-based incentive structures, the number of complaints that can be managed within the allocated time interval also decreases. However, clinicians do not disregard or drop complaints randomly. Rather, dealing with the chief complaint is usually preserved at the potential expense of postponing addressing other concerns perceived as less pressing.17,19,20 This strategy may be inadequate in the long run, in that problems deferred can ripen and return in a less manageable, more costly state.
There are a range of strategies one might pursue when dealing with diagnostic challenges as encounter time decreases. A common strategy used in many industries that deal with overtaxed resources is to try to use these resources more efficiently or off-load some of the demand to parallel resources. In the clinical setting, a way to off-load the demands of decision making and diagnostic reasoning may be to be to replace these time-intensive processes with testing or referrals, which may be done offline or asynchronously.
Measuring the face time, the part of the clinical encounter where the clinician and patient are actually together, is challenging in the clinical setting. Previous methods have ranged from surveys and administrative data to time-motion studies.
Each method has had its own set of limitations.7,8,21-26 Surveys and administrative data are subject to recall bias and anchoring or averaging of values. The latter is a particular problem because it is clearly known that variance is at least as important as average behavior, if not more so, in driving system performance.27 Timemotion studies are excellent at identifying specific measures in detail but are expensive and prone to distort subject behavior just by the presence of observers.
Real-time location systems (RTLSs) are a relatively new method that can help solve this problem. Commonly used outside of healthcare, RTLSs are a class of technologies used to identify and track the location of objects, typically in an indoor setting. Metaphorically they may be considered an indoor form of a global positioning system. As such, they hold similar potential to be used in a wide variety of ways and to serve as the infrastructure for yet-to-be-developed innovations beyond inventory and people tracking. These systems can be based on a wide variety of signaling modalities including radiofrequency (RF), infrared (IR), and ultrasound,28 and use a variety of methods to resolve the location of an object or person in time and space. In active systems, the tags themselves emit a signal that is detected by an array of receivers. In passive systems, the antennae/receivers emit a signal in a mode such as RF, the tags reflect it, and the receiver detects it. All systems have varying degrees of granularity, signal resolution, noise, and latency with regard to location and time. To date, the most common use of RTLSs both in and out of healthcare has been for inventory tracking.29-31 Part of the purpose of the Massachusetts General Hospital/Massachusetts General Physician Organization (MGH/MGPO) outpatient radio frequency identification (RFID) project has been to determine the feasibility of adapting these systems for use in busy clinical settings and monitor clinic system behavior.32,33
In this analysis we were able to use RFID to determine the trade-off between face time and diagnostic testing.
As part of the MGH/MGPO outpatient RFID project, several primary care, urgent care, and specialty clinics had RFID-based RTLSs installed. Clinicians and patients were asked to wear RTLS tags. Participation was voluntary. Flow data were derived from RTLS. Wait time was defined as the time from check-in/registration to the time the patient and clinician were colocated in the same exam room. Face time as defined as the length of time that patients and clinicians were colocated in the same room. Flow time was defined as the time from patient check-in/registration to patient checkout/ deregistration. See Figure 1. This project was approved by the MGH institutional review board.
Radiology testing was used as a proxy for medical resource use. These tests are part of routine medical practice and are relatively easy to identify from the electronic health record (EHR). The inclusion criterion was any radiology test for an adult patient that could be associated with a specific clinical encounter. This condition was met if the radiology test shared the patient’s unique identifier, if the ordering provider and face-time clinician were the same, and if it was the first radiology test to occur less than 3 months after the index encounter. Patient under 21 years old were excluded from the study. Test data were derived from the hospital research patient data registry and EHR.
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