The reduced availability of sophisticated tests and proceduresin hospitals on weekends (the so-called "weekend effect") delayscare. Addressing this problem requires hospital managers to balancethe desire for timeliness with the need for efficient operations.We illustrate how a hospital can profile timeliness, demand, andcapacity utilization across the week for multiple testing areas. Thissimple, practical method, using data extracted from the hospital'saccounting system, makes visible the pattern and magnitude ofdelays caused by reduced availability on weekends, while alsoshowing how capacity is deployed. We combined the analyticaltool with a process of transparent feedback and local problem solvingthat engages multiple stakeholders in the hospital. The goal isto optimally configure capacity so as to balance the imperatives oftimely availability and efficient resource utilization.
(Am J Manag Care. 2005;11:553-558)
For a large hospital that must deploy expensivecapacity while containing costs, the desire fortimely care and efficient operations may requirebalancing competing aims. The care in US hospitalsincreasingly revolves around sophisticated, resource-intensivetests and procedures (eg, computed tomography[CT], magnetic resonance imaging [MRI], invasivecardiovascular procedures, interventional radiology,and gastrointestinal [GI] endoscopy).1-5 On weekends,hospitals often reduce the availability of these services,thereby producing a "weekend effect" that has beenshown to prolong hospitalization,6-8 and may contributeto increased mortality observed in patients admitted onweekends.9,10
In this article, we describe how the desire for across-the-week timely availability of sophisticated tests andprocedures relates to (and may compete with) the questfor efficient use of resources. We then describe how weare balancing those aims in our institution by using apractical analytical tool that gives hospital managers acomprehensive, reproducible view of timeliness,demand, and capacity utilization for multiple testingareas throughout the hospital. We then explain how weuse the tool to support continuous improvement byengaging the providers and requestors of these proceduresas stakeholders. We believe our methods areadoptable by other organizations.
TIMELY AVAILABILITY VS RESOURCEEFFICIENCY
Delays that prolong hospitalization increase the timethat patients are exposed to hazards known to occur inhospitals, such as medication errors and infection.11Delays also negatively affect a hospital's operating economicsby adding days of hospitalization that are notreimbursed under prospective, case-rate payment.Hospitals balance their wish for "24-by-7" availabilityagainst the high costs of that availability. Most do itinformally, relying on traditional working-day hours toestablish a base schedule. Capacity has to be plannedand processes designed in ways that meet the needs ofhospitalized patients for timely care, while avoidingsuboptimal deployment of scarce, expensive resources(labor and equipment). In other words, hospitals needto efficiently translate these inputs into the outputs ofcompleted plans of care, while avoiding waste.
In any service operation (eg, a hospital's testing orprocedural laboratory), the amount of infrastructureand the associated fixed cost are functions of peakworkload.12 If time-sensitive capacity is mismatched totime-sensitive demand, artificially high peak workloadsmay require coverage with fixed-cost resources.Avoidable peaks represent waste.
Another source of waste is batch production. For aparticular testing or procedural area, the volume and timingof demand often necessitate intermittent operations,where volume is served in batches, interspersed withperiods of shutdown. If a procedural laboratory can beconceptualized like a manufacturing operation, startingeach batch from a state of shutdown incurs costs of "settingup." Personnel, machines, and materials must bepositioned and prepared for the batch, regardless ofsize.13 Excessive setups because of small batch sizes representwaste.
Our main hospital is a 1157-bed, general, acute-carefacility with approximately 40 000 admissions per year.We developed an automated data-extraction tool fromour hospital's accounting analytical system (SunriseDecision Support Manager, Eclipsys Corporation, BocaRaton, Fla). At our institution each testing area is anaccounting cost center.
For an inpatient served by a particular testing area,our accounting system counts the days elapsingbetween admission and the first billed service from thatcost center. Procedures performed the same day asadmission are counted as zero days of waiting. A patientis assumed to receive services only once from that costcenter during a hospitalization. Calculations of averagewaiting times use only the first 6 days of hospitalization.
By using the day of admission (rather than the day ofrequest) to estimate waiting time, we intended to measurethe waiting involved in executing a plan of care formulatedupon admission. This is an importantsimplifying assumption that we believe is more objectivethan measuring the interval from request to procedure,which could be influenced by the requester's perceptionsand expectations of availability. By using theadmission-to-procedure interval, we include the waitinginvolved in the communication and receipt of requests,which are elements of the overall service process.
Twice a year we download data for all the inpatientsserved by a particular testing area in the immediatelypreceding 6 months, with each data record representing1 hospital discharge. Included in each record arefields for the diagnosis-related group, principal procedure,length of stay (LOS, in days), inpatient wardservice, and emergency department admission status(yes or no).
For each 6-month dataset from each testing area(typically containing 400 to more than 3000 cases), weuse the weekday of admission to divide the patientsserved into 7 cohorts. We assume cohorts are similar interms of medical needs. Within each cohort, we counthow many patients receive procedures on the day ofadmission (called day 0) and on subsequent days, therebygenerating a time-to-performance profile. We lookfor effects of differential availability, which appeargraphically as a weekend "gap" and post-weekend "cluster"(Figure 1).
To estimate the opportunity to improve timeliness,we calculate days of potentially avoidable waiting byequalization (DPAWE) by estimating how many daysof waiting could be avoided if patients admitted onFriday, Saturday, and Sunday (who would likelyencounter weekend delays) received their procedureswith the same timeliness as patients admitted onMonday, Tuesday, and Wednesday (Thursday admissionsare omitted from the calculation). To focus on theexecution of the initial plan of care, the calculationignores procedures occurring beyond the sixth day ofhospitalization.
We produce a graphical "dashboard" for each testingarea (Figure 2), showing the profiles for the 7 admission-day cohorts, as well as the area's admission andprocedural volume by weekday. The admission volumeby weekday portrays the pattern of demand. The proceduralvolume by weekday portrays capacity utilizationrelative to the weekday with the highest volume. Thedashboard allows users to quickly see patterns of volumeand timing in order to spot gaps, lags, peaks, andsurges that may be opportunities for reconfiguringcapacity. The dashboard includes a summary "boxscore" that shows the average days of waiting and volumesused to calculate DPAWE. Dashboards also can berun for subsets of patients who were served by a testingarea—for example, particular procedure(s), diagnosis-relatedgroup(s), admission site (emergency department),or ward service.
To enable problem solving at the local level, analystswithin each testing area (typically administrators) runthe spreadsheet tool to create the dashboards, whichare then shared freely among testing areas and the hospital'sclinicians. The local analysts also perform targetedsubset analyses for their clinical leaders andmanagers, who plan capacity, make policy, and designoperating processes. A transparent, participatoryapproach allows stakeholders to find a balance betweentimeliness of service and efficient use of resources.
An institutional committee maintains oversight, setsexpectations for improvement, and facilitates dialoguebetween the testing areas ("suppliers") and the clinicians("customers"). Managers are asked to focus onareas with high volumes and high DPAWE scores. Thecommittee also oversees the exchange of best practicesand innovation for ordering, scheduling, and staffing.Typical questions addressed in these dialogues include:
"Do the data confirm prior impressions of timelinessand availability?"
"Could more timely admission assessment andordering mitigate delays?"
"Would a Saturday procedural session (eg, a half-day)substantially mitigate delays? For the anticipatedvolume, what would be required for staffing andupstream/downstream coordination?"
The tool provided our hospital leaders and managerswith their first comprehensive view of timeliness acrossthe week for high-impact testing procedures throughoutthe hospital. In some areas, waiting time for testsordered for patients admitted late in the week (Friday,Saturday, Sunday) appeared to improve modestly afterthe tool was implemented in 2002. For example, forechocardiography, the average number of days fromadmission to procedure decreased from 1.7 in 2002to 1.5 in 2004. For body CT, the number of daysdecreased from 1.2 in 2002 to 0.9 in 2004; for GIendoscopy, the decrease was from 2 days in 2002 to1.7 days in 2004. In most areas timeliness remainedabout the same or improved slightly from 2002 to2004, despite growth in volume in some areas (datanot shown). For many areas (echocardiography,cardiac catheterization, neurological MRI, GI endoscopy)the disparity in average waiting timesbetween late-week and early-week admissionsappeared to modestly decrease.
The profiling technique allowed us to see how wellthe admission volume by weekday that used a particulartesting area ("demand") aligned with its proceduralvolume by weekday ("capacity," relative tothe peak weekday). In areas with excellent timelinessacross the week, the procedural-volume profile closelyresembled the admission-volume profile. However,in areas with weekend-related delays, the procedural-volume profile looked very different from theadmission-volume profile. We found a relative burstof activity on Mondays (from cases held over theweekend) and a burst on Fridays (presumably tryingto "clear the decks" ahead of the weekend)(Figure 3).
Rather than mandating specific weekend schedules,we designed a more locally driven process thatengages stakeholders who have clinical-leadership,managerial, provider, and front-line productionroles. Nearly all users have commented that thetool is easy to understand and helps them appreciatethe magnitude of opportunities. In response toour method, several areas plan to expand availabilityon weekends or evenings, guided by cost-benefitanalysis made possible by using the tool to betterunderstand differential waiting, demand, and capacity.Areas also are looking at ways to do proceduresearlier in order to alleviate high peak-volume weekdays(typically Mondays) and reduce associatedfixed-cost resources.
Using administrative data, we developed an analyticaltool that profiles timeliness, demand, and capacityutilization for sophisticated tests and procedures acrossthe week, thereby making visible the pattern and magnitudeof delays caused by reduced availability on weekends.We deployed the tool within the cooperativemanagement structure of our hospital to engage thestakeholders who would produce change. Because thedata are readily accessible from the hospital's accountingsystem, we believe other hospitals could easily performthis profiling.
Previously published efforts to improve availabilityof inpatient testing and procedures to expedite carehave addressed 1 testing area at a time.14-16 In contrast,we have introduced a more comprehensive system ofassessing and improving timeliness and capacity managementfor multiple areas. The transparency, objectivity,and simplicity of our method help identifyproblems and facilitate a cooperative approach towardsolving them.
Our analytical method is similar to that of Bell andRedelmeier, who documented weekend-related delaysfor selected urgent procedures for emergently hospitalizedpatients in the Canadian province of Ontario.17Using administrative data, they measured the days waitingfrom admission to procedure and showed how waitingvaried according to weekday of admission.
Our administrative data captured timing detail at thelevel of the day of service. We did not use medicalrecord data or hourly service logs (which could paint amore detailed picture of testing timeliness), althoughwe encourage managers to tap into (or prospectivelygenerate) these types of data locally to facilitate problemsolving.
A sufficiently long run of administrative data shouldreveal patterns of timeliness, demand, and capacity utilizationthat are meaningful enough to justify changes incapacity and availability. Because follow-up measurementusing administrative data is fast and inexpensive,managers can implement changes, assess results, makeadjustments, and later repeat the measurement.
Although reducing the waiting for a given test or procedureshould contribute to shortening LOS, it may bedifficult to predict the aggregate LOS reduction attributableto reconfiguring 1 area. The relationship of 1testing area to LOS is sometimes straightforward (eg, apatient admitted with angina who needs only cardiaccatheterization and angioplasty). More often, the relationshipof the testing area to LOS is more indirect,because it is part of a sequence of contingent steps (eg,a patient admitted with GI bleeding who is found tohave a gastric cancer, but then needs surgery). Waitingfor testing has been shown to be an important cause ofdelayed care in a large teaching hospital, but there aremany other causes.18
Removing a "bottleneck" in 1 area may reveal anotherdownstream impediment. Multiple areas of the hospitalmay need to improve simultaneously to accelerate thecare of large numbers of patients. We believe our methodhas the advantage of being able to examine multiple areassimultaneously using a uniform, systemic approach.
Because every hospital has its own combination ofoperating configuration, cost functions, and stakeholderdynamics, finding the optimal balance between timelinessof service and efficient resource utilization probablydefies a formulaic "cookbook" solution. We areusing a local approach to problem solving, in which thepersons most intimately familiar with operations developsolutions and implement change, guided by a toolthat provides easy-to-understand feedback and reinforcement.We believe that the insights revealed by thistransparent, intuitive analysis help clinical leaders,managers, and front-line personnel work together todevelop optimal solutions.
We wish to thank David A. Asch, MD, MBA (Leonard Davis Institute ofHealth Economics, University of Pennsylvania) and David C. Herman, MD(Clinical Practice Committee, Mayo Clinic) for helpful comments on thismanuscript. We also thank Larry D. Albrecht (Department of Systems andProcedures, Mayo Clinic) for assistance with data collection and Jaysen P.Ness, Julie A. Lisowski, and Tina K. Sass (Rochester Financial Analysis,Mayo Clinic) for ongoing analytical support.
From the Department of Internal Medicine (LHL, CAG, DLW), the Department ofRadiology (SJS), and Rochester Financial Analysis (RRM), Mayo Clinic and Foundation, 200First Street SW, Rochester, Minn.
Address correspondence to: Lawrence H. Lee, MD, MBA, Department of InternalMedicine, Siebens 660, Mayo Clinic, 200 First Street SW, Rochester, MN 55905. E-mail:email@example.com.
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