The Effect of Utilization Review on Emergency Department Operations.

The Impact of Utilization Review on Emergency Department Operations - PMC Skip to main content An official website of the United States government Here's how you know Here's how you know Search Log in Primary site navigation Search Log in Search PMC Full-Text Archive Search in PMC Journal List User Guide As a library, NLM provides access to scientific literature. Inclusion in an NLM database does not imply endorsement of, or agreement with, the contents by NLM or the National Institutes of Health. Learn more: PMC Disclaimer | PMC Copyright Notice Ann Emerg Med . Author manuscript; available in PMC: 2026 Jun 2. Published in final edited form as: Ann Emerg Med. 2017 May 27;70(5):623–631.e1. doi: 10.1016/j.annemergmed.2017.03.043 The Impact of Utilization Review on Emergency Department Operations Shoma Desai 1 , Phillip F Gruber 1 , Erick Eiting 1 , Seth A Seabury 1, 2 , Wendy J Mack 3 , Christian Voyageur 4 , Veronica Vasquez 1 , Hyung T Kim 1 , Sophie Terp 1, 2 Author information Article notes Copyright and License information 1 Department of Emergency Medicine, Keck School of Medicine, University of Southern California, Los Angeles, California 90033 2 Leonard D Schaeffer Center for Health Policy and Economics, University of Southern California, Los Angeles, California 90089 3 Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, California 90033 4 Los Angeles County + University of Southern California Medical Center, Los Angeles, CA 90033 Author Contributions Drs. Desai, Gruber, Kim, Eiting, and Vasquez conceived and designed the study, and wrote the IRB application. Drs. Desai, Gruber, and Vasquez obtained research funding. Drs. Gruber, Kim, and Terp worked with Mr. Voyageur to extract data and perform basic analysis. Drs. Mack and Seabury worked on advanced data analysis and statistics. Dr. Desai drafted the manuscript and all authors contributed substantially to its revision. Dr. Desai takes responsibility for the paper as a whole. Issue date 2017 Nov. PMC Copyright notice PMCID: PMC13225766  NIHMSID: NIHMS2160908  PMID: 28559030 The publisher's version of this article is available at Ann Emerg Med Abstract Background Increasingly, hospitals are using utilization review (UR) software to reduce hospital admissions in an effort to contain costs. Such practices have the potential to increase the number of unsafe discharges, particularly in public, safety-net hospitals. UR software tools are not well-studied with regard to their impact on emergency department (ED) operations. Objective To study the effect of prospectively used admission decision support on ED operations. Research Design In 2012, LAC+USC Medical Center implemented prospective use of computerized admission criteria. Following implementation, only ED patients meeting primary (diagnosis-based criteria) or secondary (medical necessity as determined by an on-site emergency physician) review were assigned inpatient beds. Data were extracted from electronic medical records from September 2011 through December 2013. Outcomes included operational metrics, 30-day ED revisits, and 30-day admission rates. Excluding a 6-month implementation period, monthly summary metrics were compared pre- and post-implementation using non-parametric and negative binomial regression methods. Subjects All adult ED visits, excluding incarcerated patients and purely behavioral health visits, were analyzed. Measures The primary outcomes were disposition rates. Secondary outcomes were 30-day ED revisits, 30-day admission rate among return visitors to the ED, and estimated cost. Results Analysis of 245,662 ED encounters was performed. The inpatient admission rate decreased from 14.2% to 12.8%. Increases in discharge rate (82.4% to 83.4%) and ED observation unit (EDOU) utilization (2.5% to 3.4%) were found. Thirty-day revisits increased (20.4% to 24.4%) though the 30-day admission rate decreased (3.2% to 2.8%). Estimated cost savings totaled $193.17 per ED visit. Conclusions The prospective use of UR software in the ED led to a decrease in the admission rate. This was tempered by a concomitant rise in EDOU utilization and 30-day ED revisits. Cost savings suggest that resources should be redirected to the more highly impacted ED and EDOU, though more work is needed to confirm the generalizability of these findings. Introduction Since the Social Security Act of 1965, the Centers for Medicare and Medicaid Services (CMS) has issued retrospective payment denials for an increasing number of medical services deemed inappropriate. 1 In 2013, CMS reported an improper payment rate of 8% for inpatient hospital services, with an estimated cost of approximately $9.4 billion. 2 These errors in reimbursement account for nearly a quarter of the overall Medicare Fee-for-Service improper payment rate and are a focus of the national call to reduce excessive healthcare expenditures. 1 , 2 As one solution to this crisis, utilization review (UR) has been increasingly employed by hospitals, managed care organizations, as well as public and fee-for-service payers to ensure accuracy of care, time, place, and cost. 3 , 4 To align with CMS Recovery Audit Contractors by identifying inpatient stays that may subsequently be deemed ‘inappropriate’, hospital systems across the United States are incorporating commercial evidence-based admission decision support software into their daily operations. Such decision support tools are intended to reduce the number of denied days, minimize variations in care across hospital systems through standardized criteria, and improve transparency between healthcare providers and payers. 5 They may be employed in a prospective manner, such that medically unnecessary stays are avoided by screening prior to admission, or more commonly in a concurrent manner, such that inpatient admissions are reviewed daily to reduce denied days and optimize the level of care. While the use of UR is increasingly widespread, there is concern that it could potentially over-reach and prioritize cost containment possibly at the expense of patient outcomes. 15 In 1989, the Institute of Medicine urged researchers to study the impact of utilization management on the delivery of patient care. 6 Since then, few studies have been published on the impact of UR on cost containment, achieved through a reduction of admissions and denied days. 16 - 18 , 21 As a result, relatively little is known about the influence of UR on the quality of patient care, patient safety, and operations. In this study, we examine the effect of prospective admission decision support software on patient disposition and 30-day ED revisits at a large, urban safety-net hospital over a 22-month period. Methods Los Angeles County + University of Southern California (LAC + USC) Medical Center is an urban, public, safety-net hospital, with an emergency department (ED) patient volume of approximately 170,000 visits annually. In the fall of 2012, LAC + USC Medical Center implemented admission decision support via InterQual ® (version 2012 and 2012.2) by McKesson. Previously, the California Department of Health Care Services (DHCS) used a Treatment Authorization Request (TAR) process to perform 100% utilization review for Medicaid Fee-for-Service (inpatient stays). In 2008-2010, DHCS piloted and expanded a program using standardized, evidence-based review criteria through decision support software. This software package is linked in real time with the admissions process as follows: Using the admission decision support tool, all requests of admission are screened by UR nurses located within the ED 24 hours a day. Admissions that meet decision support tool appropriateness criteria are allowed to proceed with bed assignment. Admissions that do not meet appropriateness criteria are referred to another on-site emergency physician to review for medical necessity. Cases deemed medically necessary on secondary review are then allowed to proceed with bed assignment. All patients being considered for admission were screened by UR nurses utilizing this software regardless of insurance (e.g. Medicare, Medicaid, uninsured). As some private insurers (HMOs) do not require prospective admission review as a condition of reimbursement for inpatient stays, criteria were not applied in stabilized patients authorized for transfer to hospitals covered by private insurance. Patients who undergo and do not pass secondary review for medical necessity are either discharged or observed in an observation unit. Decision support criteria for observation level of care were not utilized. This retrospective study included all adult patient (age 18 and above) visits to the LAC+USC Medical Center ED from September 2011 through December 2013. Incarcerated patients and pure behavioral health visits were excluded from this analysis. The study was approved by the University of Southern California Institutional Review Board. Implementation of this UR process was initiated in the fall of 2012 in a stepwise fashion, starting with informal admission review and provider training. The formal review process, involving a “hard stop” on bed assignments pending approval, started in January 2013. To allow for a clear comparison between pre- and post-implementation operations, patient visits in the 6-month roll-out period (August 1, 2012-January 31, 2013) were excluded. Appendix A depicts the number of ED visits, exclusions by category, and final number of visits analyzed. The following operational data, summarized by month, were abstracted from the ED Information System (Wellsoft ™ ): patient demographics (monthly average age and gender distribution), initial Emergency Severity Index (ESI) score, ED volume, average ED length of stay (LOS, defined as arrival to departure time), observation unit LOS (defined as arrival to ED to departure from the observation unit), 30-day ED revisit rate, and 30-day admit rate. The 30-day admit rate was defined as the admission rate for patients returning to the ED within 30 days. The 30-day revisit and admit rates were calculated for two groups: (1) all patients visiting the ED and (2) limited to patients discharged from the ED during the first visit. Metrics from the Hospital Information System (QuadraMed Affinity ® ) included: inpatient level of service (i.e. unmonitored vs monitored), hospital admission rate, observation unit placement rate, transfer rate to other facilities, and hospital LOS. Of note, “unmonitored” beds were defined as inpatient ward beds while “monitored” beds included telemetry, step down, and ICU levels of care. Established in 2008 (prior to study start date), the observation unit at this facility is split into surgical (11 beds) and medical (17 beds) areas. Both observation units are staffed by nurse practitioners and/or physician assistants (both under the supervision of attending physicians) and nurses, with social work services available daily. There were no significant changes to the capacity or staffing of observation units throughout the study period. To control for process changes that may have occurred over the study period unrelated to the implementation of UR, syncope and appendicitis were chosen as comparative conditions for analysis, utilizing ED discharge ICD-9 codes. Syncope is a complex condition with multiple etiologies, ranging from benign to life threatening. The decision to admit patients with this condition to the hospital is subject to a substantial degree of practice variation. For example, an otherwise healthy 28-year old female presenting with syncope will often be discharged if diagnostic tests do not reveal any concerning findings. However, a 65-year old female with multiple comorbidities and unremarkable diagnostic test results may have a different disposition, depending on the ability to get timely outpatient evaluation and on the ED physician’s clinical impression. Thus, admission rates for syncope would be expected to be sensitive to standardized criteria. InterQual ® admission criteria (version 2014) for syncope of unknown etiology require one or more of the following findings: structural or functional cardiac disease, ECG abnormalities, family history of sudden cardiac death, symptoms preceding syncope, and syncope during exertion or while supine. Also, management must involve both cardiac monitoring and syncope evaluation, which necessitates carotid artery massage (unless contraindicated), ECG, echocardiogram (unless recently performed), and orthostatic blood pressure measurement. 7 Rates of admission for appendicitis, a condition for which patients are nearly universally admitted to the hospital, would not be expected to be influenced by admission criteria. Appendicitis, therefore, was chosen as a control condition. Metrics were compared before (September 2011- July 2012) and after (February 2013- December 2013) the implementation of the decision support tool, with monthly summary metrics as units of analysis. A dichotomous variable for pre- and post-decision support implementation was the primary independent variable to test for differences in metrics. Comparisons between pre- and post-implementation periods on age and other continuous outcomes (such as ED metrics) used analysis of variance and non-parametric comparisons. Outcome variables representing counts (hospital LOS), rates, and proportions were tested for mean differences pre- and post-decision support implementation using negative binomial regression due to Poisson over-dispersion in many of the outcomes. In most cases, the denominator for rate variables was the monthly ED volume. Thirty-day revisit and admission rates were compared among all ED patients (using monthly ED volume as the denominator) and among patients discharged from the ED (using monthly ED discharges as the denominator). Appendix B reveals a segmented regression for the interrupted time series used to estimate admission decision support in the face of ongoing secular trends in each of the metrics. Metric measures were regressed against time (month), adding an estimate for the acute change in level (change in the mean) from the end of the pre-implementation to the start of the post-implementation periods. Both the immediate effect (change in level in the regression line and linear trends (slopes, estimated as monthly trends) in metrics were estimated and compared pre- and post-implementation. Results are reported as estimates (means, slopes) with 95% confidence intervals. Trends were graphically displayed by plotting monthly summary statistics over time ( Figure 1 ). Figure 1: Trends in Monthly Admission, Discharge, Observation and 30-Day Revisit Rates. Open in a new tab Monthly summary data plotted over the course of the study period. The red lines delineate the 6- month implementation period (8/1/12-1/31/13). Lowess curves are depicted. A simple, approximate cost analysis was performed using the change in rates of admission, observation unit utilization, and re-visits to the ED between the pre- and post-implementation periods. The average cost per person of an inpatient stay and ED visit were obtained from the Agency for Healthcare Research and Quality’s (AHRQ) Medical Expenditure Panel Survey (MEPS). 8 The MEPS is a nationally representative survey that combines information from households, individuals, insurers and medical providers, and it represents the most complete, publicly available source of data on healthcare utilization and spending in the US. The cost of an observation unit stay per person was obtained from the CMS Outpatient Prospective Payment System 2013 Rule, which lists Medicare reimbursement rates for specific services. 9 For this study, we used the base reimbursement for an observation unit stay (Ambulatory Payment Classification 8009) and added the average payment for an ED stay. Results From September 2011 through December 2013, there were a total 406,936 ED visits. Excluding pediatric, incarcerated, and behavioral health visits and a 6-month implementation period, 245,662 patient encounters were analyzed. (Please see Appendix A for reference) Table 1 shows monthly patient volumes and demographics. The average number of adult ED visits was 11,028 per month before implementation and 11,305 per month following implementation. The mean age, sex, and distribution of initial Emergency Severity Index (ESI) scores are also listed in Table 1 . During the pre-implementation period, encounters by payer were as follows: 60.9% uninsured, 30.2% Medicaid, 4.9% Medicare, 2.6% commercial insurance, and 1.4% other. During the post-implementation period, encounters by payer were as follows: 61.0% uninsured, 29.9% Medicaid, 5.3% Medicare, 2.6% commercial insurance, and 1.1% other. Table 1: Monthly Patient Volume and Demographics Descriptives by month for all adult emergency department (ED) visits during the pre- implementation (9/11-7/12) and post-implementation (2/13-12/13) periods. The table includes monthly summary data and 95% confidence intervals. Variable Pre-Implementation (95% CI) Post-Implementation (95% CI) ED Volume (# visits) 11,028.4 (10733.1, 11323.6) 11,304.6 (11002.0, 11607.0) Mean Age (yea rs) 44.8 (44.7, 44.9) 45.4 (45.2, 45.6) Female 45.8% (45.4%, 46.3%) 45.5% (45.1%, 46.0%) Initial Emergency Severity Index (ESI) Score ESI 1 1.4% (1.3%, 1.5%) 1.2% (1.1%, 1.3%) ESI 2-3 76.3% (75.6%, 77.0%) 74.2% (73.5%, 74.9%) ESI 4-5 22.3% (21.7%, 22.9%) 24.6% (23.9%, 25.3%) Open in a new tab Table 2 summarizes monthly lengths of stay, disposition rates, and return visits. The mean ED LOS and mean hospital LOS were approximately 8.3 hours and 6.5 days, respectively, throughout the study period. Before implementation of admission criteria, ED discharges comprised 82.4% (95% CI 81.8%, 82.9%), observations 2.5% (95% CI 2.3%, 2.7%), admissions 14.2% (95% CI 13.6%, 14.8%), and transfers 0.9% (95% CI 0.9%, 1.0%) of dispositions. After the implementation of admission criteria, the ED experienced increased rates of discharge (83.4%, 95% CI: 82.8%, 83.9%) and observation unit utilization (3.4%, 95% CI: 3.1%, 3.6%). Noteworthy is an increase in observation unit length of stay from 24.4 hours (95% CI 23.5, 25.2) to 27.1 hours (95% CI 26.4, 27.8). Meanwhile, the rates of admission (12.8%, 95% CI: 12.3%, 13.4%) and transfer to outside hospitals decreased (0.5%, 95% CI 0.5%, 0.6%). Table 2: Patient Dispositions Throughput, disposition rates, and revisits within 30 days for all adult emergency department (ED) visits during the pre-implementation (9/11-7/12) and post-implementation (2/13-12/13) periods. Numbers in table are regression estimates (95% confidence intervals). The 30-day admit rate is the admission rate on the second visit to the E D. The discharged sample includes only adult patients discharged from the ED on the first visit. Variables Pre-Implementation Mean Post-Implementation Mean ED LOS (hours) 8.1 (7.8, 8.5) 8.5 (8.1, 9.0) Hospital LOS (days) 6.5 (6.1, 6.9) 6.5 (6.2, 6.7) Observation Unit LOS (hours) 24.4 (23.5, 25.2) 27.1 (26.4, 27.8) Hospital Admit Rate (total) 14.2% (13.6%, 14.8%) 12.8% (12.3%, 13.4%) Hospital Admit Rate: Unmonitored 10.9% (10.6%, 11.3%) 9.3% (9.0%, 9.6%) Hospital Admit Rate: Monitored 3.3% (3.0%, 3.6%) 3.5% (3.2%, 3.8%) Discharge Rate 82.4% (81.8%, 82.9%) 83.4% (82.8%, 83.9%) Transfer R ate 0.9% (0.9%, 1.0%) 0.5% (0.5%, 0.6%) Observation R ate 2.5% (2.3%, 2.7%) 3.4% (3.1%, 3.6%) 30-Day Revisit Rate (total) 20.4% (19.9%, 20.9%) 24.4% (23.8%, 25.0%) 30-Day Admit Rate (total) 3.2% (3.1%, 3.4%) 2.8% (2.7%, 3.0%) 30-Day Revisit Rate (discharged) 20.3% (19.8%, 20.9%) 24.9% (24.3%, 25.6%) 30-Day Admit Rate (discharged) 2.3% (2.1%, 2.4%) 1.9% (1.8%, 2.0%) Open in a new tab In the pre-implementation period, approximately 10.9% (95% CI 10.6%, 11.3%) of adult ED patients were admitted to unmonitored beds while 3.3% (95% CI 3.0%, 3.6%) were admitted to monitored beds. After implementation, there was a decrease in admissions to unmonitored beds (9.3%, 95% CI: 9.0%, 9.6%) while the percentage admitted to monitored settings did not change (3.5%, 95% CI: 3.2%, 3.8%). ( Table 2 ) Prior to the implementation of admission criteria, the 30-day revisit rate to the ED was 20.4% (95% CI 19.9%, 20.9%). The admission rate on the second visit was 3.2% (95% CI 3.1%, 3.4%). After implementation, the overall 30-day revisit rate to the ED increased to 24.4% (95% CI 23.8%, 25.0%). The admission rate on the second visit decreased to 2.8% (95% CI 2.7%, 3.0%). These findings were similarly reflected by the subgroup of patients discharged from the ED on the first visit. ( Table 2 ) To clarify the impact of the admission decision support tool on ED operations, a comparative analysis of patients diagnosed with syncope (hypothesized to be sensitive to decision support tool implementation) and appendicitis (hypothesized to be insensitive to decision support tool implementation) was performed. ( Table 3 ) There were similar numbers of ED visits for syncope before and after implementation. However, the inpatient admission rate for syncope decreased from 16.9% (95% CI 13.8%, 20.7%) to 11.4% (95% CI 8.9%, 14.5%) while observation unit utilization increased from 12.1% (95% CI 9.2%, 15.9%) to 20.7% (95% CI 16.5%, 25.9%). The discharge rate and 30-day revisit rate of this subgroup increased slightly. The admission rate for patients returning to the ED within 30 days decreased from 2.7% (95% CI 1.6%, 4.5%) to 1.1% (95% CI 0.5%, 2.4%) after implementation. Table 3: Syncope and Appendicitis Sub-Group Analysis Monthly ED volumes, lengths of stay, disposition rates, and overall 30-day revisit and admission rates are shown for all adult ED patients diagnosed with syncope or appendicitis during the pre- implementation (9/11-7/12) and post-implementation (2/13-12/13) periods. The table includes monthly summary data and 95% confidence intervals. Variable Syncope Appendicitis Pre- implementation (95% CI) Post- implementation (95% CI) Pre- implementation (95% CI) Post- implementation (95% CI) E D Volume 50.5 (45.6, 55.3) 51.2 (46.2, 56.1) 38.8 (35.1, 42.6) 37.2 (33.5, 40.9) ED S tay (hours) 11.7 (11.1, 12.2) 13.8 (12.2, 15.5) 13.1 (11.8, 14.4) 16.3 (14.3, 18.2) Hospital LOS (days) 2.3 (1.7, 2.9) 2.7 (1.6, 3.8) 2.6 (2.3, 2.9) 3.1 (2.6, 3.5) Admission Rate 16.9% (13.8%, 20.7%) 11.4% (8.9%, 14.5%) 96.7% (87.8%, 100%) 96.8% (87.7%, 100%) Discharge Rate 60.4% (54.2%, 67.2%) 62.3% (56.2%, 69.2%) 3.0% (1.8%, 5.2%) 1.5% (0.7%, 3.3%) Transfer Rate 10.6% (8.2%, 13.7%) 5.5% (3.9%, 7.8%) 0 1 0 1 Observation Rate 12.1% (9.2%, 15.9%) 20.7% (16.5%, 25.9%) 0.2% (0.0%, 1.7%) 1.7% (0.8%, 3.6%) 30-Day Revisit Rate 12.3% (9.7%, 15.5%) 13.9% (11.1%, 17.3%) 10.6% (7.8%, 14.3%) 9.0% (6.5%, 12.6%) 30-Day Admission Rate 2.7% (1.6%, 4.5%) 1.1% (0.5%, 2.4%) 2.3% (1.2%, 4.5%) 2.7% (1.5%, 5.0%) Open in a new tab 1 No transfers in either pre- or post-implementation periods. There was no statistical difference in the number of patients diagnosed with appendicitis per month throughout the study period. The great majority were admitted both before and after implementation. The rates of discharge, transfer, 30-day revisits, and admission on return visit were not significantly different in the pre- and post-implementation periods. ( Table 3 ) Table 4 demonstrates the results of the cost analysis. Multiplying the average reimbursement for an emergency room visit ($1,762), inpatient stay ($20,758), and observation (cost of ED visit ($1,762) plus cost of observation unit stay ($1234) = $2,996) by the difference in ED revisits in 30 days, admission rate, observation rate, respectively, associated with the adoption of UR review. For example, per the 2014 MEPS, the average payment for a hospitalization was $20,758, so the reduction in admission rate yields an expected saving of $290.61 per ED visit. Combining this with the offsetting effect of a higher revisit rate and greater use of observation unit beds, the overall net cost savings were estimated to be $193.17 per ED visit. Based on the average number of adult ED visits per month after implementation (11,305), the projected total aggregate savings would have been $26,204,515 per year in our hospital setting. Table 4: Cost Analysis 8 , 9 An approximate cost-analysis based on percent change in admission rate, emergency department revisit rate (within 30 days), and observation unit utilization. Estimates of cost are extracted from the AHRQ Medical Expenditure Panel Survey (Western census region) 8 and the CMS Outpatient Prospective Payment System 9 . The observation payment includes the cost of an ED visit ($1,762) and the cost of an observation stay ($1,234). Variable Pre- Implementation (%) Post- Implementation (%) Change (%) * Mean Payment per Person 8,9 Cost per Visit ** Admissions 14.2 12.8 −1.4 $ 20,758 −$290.61 Emergency Department Re-visits (30- days) 20.4 24.4 + 4.0 $ 1,762 + $70.48 Observations 2.5 3.4 + 0.9 $ 2,996 + $26.96 Net cost per visit −$193.17 Open in a new tab * The difference between pre- and post-implementation monthly percentage rates ** Approximate cost multiplied by percentage change in variable The pre-post analysis discussed above is useful to test whether there was an association between the adoption of the policy and post-implementation outcomes. However, one limitation of the pre-post design is that it assumes there is no confounding trend. To test this, we graphically display the pre- and post-implementation trends by month over the duration of the study period in Figure 1 . In general, there is no clear evidence of pre-existing trends in the three months before or after implementation, though there is some general decline in admissions around 6-9 months prior to implementation. Additionally, there appears to be a slight drift toward a return to pre-implementation rates over time. Limitations There are some limitations to this study. First, this is a single center study, involving a publicly funded, safety-net ED. Our institution serves patients with limited access to primary care and those with social and/or psychiatric issues. While this is a unique patient population, the large volume of patient encounters studied strengthens our findings and may be applicable to other public, urban, safety-net facilities. Second, results were likely influenced somewhat by coding error, a limitation inherent to computerized data extraction. For example, we found a 1.5-3.0% discharge rate among patients with ICD-9 codes consistent with appendicitis, a condition for which inpatient admission is expected. Upon review of a random sample of these discharged patients, it was discovered that the majority of these discharged patients were diagnosed with epiploic appendagitis, a benign condition which rarely requires inpatient admission or surgery. Also, the data were analyzed by month. Though this is the usual format both operational and quality assurance reporting, this time interval may be less precise than more granular data in reflecting variations by day of the week or month of the year. However, we do not expect that such errors are likely to affect our overall results. Third, our data may have been affected by other operational changes implemented during the study period. To better view the influence of confounding trends, we performed a comparative analysis of patients diagnosed with syncope (deemed admission criteria sensitive) and appendicitis (deemed admission criteria insensitive). Our hypothesis was that the dispositions of syncope patients would be greatly influenced by an admission decision support tool while those of appendicitis patients would be relatively unchanged. As anticipated, the syncope admission rate decreased approximately 5.5% while the observation rate increased by approximately 8.6%. Meanwhile, the appendicitis disposition rates stayed relatively steady throughout the study period. While the presence of confounding trends may affect the results of ‘before/after’ studies, this comparative analysis is supportive of our conclusions. Finally, detailed cost reporting was unavailable for a more sophisticated cost analysis. This was caused in part by a difference in record-keeping after the employment of UR software as well as this county facility’s use of flat-rate billing. Discussion In this study, we sought to answer the question of how prospective use of a contemporary utilization review tool impacts the operations of a large, public emergency department. Examining computerized decision support software, the most contemporary iteration of utilization review, we found a marked shift in patient dispositions from the ED. Most notable is a statistically significant drop in the monthly admission rate, from 14.2% to 12.8%, after implementation of prospective UR. The decrease in admission rate was accompanied by an increase in discharge rate, observation unit utilization, observation unit length of stay, and in short term return visits to the ED. The decrease in admission rate corroborates results from a few previously published studies examining the impact of more traditional UR programs. 10 - 14 Feldstein, et al, reported a statistically significant reduction in hospital admissions (12.3%) with a pre-admission and concurrent on-site utilization review program. Associated with this decline in hospital admissions was a net decrease in total medical expenditures of approximately 8% per insured person. 10 Wickizer et al, showed a similar reduction in admission rates and total expenditures with the use of UR among patients covered by 223 commercial insurance companies over a 3-year period. 11 , 12 While some proponents of UR programs have equated the systematic review of admissions for appropriateness and optimization of level of care with good patient care, one could argue that return visits and hospitalizations are more direct markers of quality. In 1997, Milstein questioned the continued reduction of health care utilization without clear evidence of maintained patient safety. 15 Soon after, Wickizer, et al, published a series of studies on privately insured patients under utilization management. In these studies, pediatric patients, mental health patients, and those admitted for cardiovascular procedures experienced an increased relative risk of readmission within 60 days when the length of stay was restricted. 16 , 17 , 18 After implementation of prospective utilization review at our institution, a greater percentage of patients were discharged from the ED only to return within 30 days. In fact, the overall 30-day ED revisit rate rose from 20.4% pre-implementation to 24.4% post-implementation. One could argue that admission criteria serve only to guide physician decision making and that, as in our case, medical necessity may override ‘failures’ of primary review. So, why did we see an increase in discharge rate post-implementation? One possible explanation is the application of standard criteria to a previously unchecked process. Prior to prospective UR implementation, admission decisions on stable “soft call” patients were unopposed. Afterward, UR nurses were available 24/7 to provide real time feedback. Another possibility for the increase in discharge rate is a change in physician behavior affected by the sheer awareness of administrative oversight. Though there were no incentives (or disincentives) assigned to the secondary peer review process, physicians may have been indirectly influenced by the accountability inherent in both documentation and oversight by their peers. While ED revisits within 30 days increased, it is important to note that a smaller percentage of patients were actually admitted during the second ED visit, indicating that returning patients were not necessarily inappropriately discharged on the first visit. Presumably, if patients were prematurely discharged during the first ED visit, the return visit should necessitate an admission either due to higher acuity or to medical necessity. In fact, Wickizer, et al, reported an increased relative risk of readmission within 60-days after implementation of a UR program. 16 - 18 This disagreement may be explained by their focus on concurrent review (i.e. review of inpatient days) as opposed to prospective review (i.e. review of admission appropriateness), an evolution in the evidence base of admission decision support criteria, or a difference in patient population. Our medical center serves a unique patient population with a substantial proportion of uninsured, homeless, and psychiatric patients. Disposition decisions are often made within the context of limited access to primary care, poorly managed chronic conditions, and social obstacles for safe discharge. Though cost-cutting measures are expected to result in reduced overall healthcare costs, the statistically significant increase in 30-day revisits found in this study must be weighed carefully against potential benefits. Moreover, we did not have access to data at a county level and, therefore, cannot rule out the possibility that the higher discharge rate was associated with negative patient outcomes. Previously, our institution bore financial losses from denied days recognized in a retrospective and delayed fashion. To reach a Treatment Authorization Request (TAR)-free state, admission decision support software with the support of UR nurses was instituted on a 24/7 basis. To determine the overall financial impact of this UR program, we performed a simplified cost analysis by multiplying the percent change in admission rate, observation unit utilization, and ED revisits with the average reimbursement of an inpatient stay, observation stay, and ED visit respectively. 8 - 9 The balance was an overall cost savings of approximately $193.17 per visit. Such monetary savings may be offset by the cost of the UR program itself. We were unable to obtain the cost of software implementation and maintenance, personnel hiring and training, or physician peer review time for a more comprehensive cost analysis. In 2003, Murray, et al, estimated a cost of approximately $300,000 annually (for both providers and payers) to support a UR program at an urban, mid-west hospital (approximately 14,000 concurrent reviews/ year). 20 Adjusting for inflation, the cost of $300,000 amounts to just over $385,000 in 2014 (the date of the MEPS/ CMS payment data) or approximately $400,000 in 2017 (the date of this study’s publication). In 1991, Wickizer, et al, also reported an increase in outpatient expenditures with hospital-based UR programs. 21 Though limited in scope, Wickizer’s study found that the reduction in hospital expenditures still outweighed the 20% increase in outpatient expenditures, resulting in net hospital savings. 21 Calculating a projected total aggregate savings of over $26 million per year, it would be safe to expect a net cost saving to our healthcare system. With this in mind, one must consider the reallocation of resources beyond the financial support of the UR program itself. In addition to the increase in 30-day revisits to the ED, there was a synchronous rise in the rate of observation unit placement (2.5% to 3.4%) and in median observation unit length of stay (24.4 hours to 27.1 hours) post-implementation. As the observation unit falls under the purview of Emergency Services at our hospital, such repercussions contribute to an additional workload for the ED. As an increasing number of hospitals across the United States implement utilization programs to reduce inpatient admissions, short stays that are under-reimbursed are likely to rise. Anticipating this shift by redirecting resources from inpatient care to emergency and observation services is prudent. However, more work needs to be done to verify both the accuracy and generalizability of these cost saving estimates. In reviewing monthly disposition and 30-day revisit rates plotted over time ( Figure 1 ), there is a discernable trend toward the end of the post-implementation period of a return to the pre-implementation baseline. Though the changes in this interrupted time series are sustained through the study period, this finding indicates that further study is needed to determine the long term effects of UR software on operations. Conclusion While noting a decrease in admission rate, this study reveals a concurrent increase in observation unit utilization, observation unit LOS, and 30-day ED revisit rate. These effects influence the patient experience, exacerbate the burden on the ED, and temper overall cost savings. While decreased healthcare costs are of national importance and translate to affordable healthcare for the individual, cost-cutting directives must be balanced with adjusted resource allocation to drive achievement of quality medical care. Supplementary Material Appendix A Appendix A: Data analysis flowchart, including initial number of ED visits, exclusions, and final number analyzed. NIHMS2160908-supplement-Appendix_A.pdf (26.4KB, pdf) Appendix B Appendix B: Throughput, disposition rates, and revisits within 30 days for all adult emergency department visits during the pre-implementation (9/11-7/12) and post-implementation (2/13-12/13) periods. Numbers in table are regression estimates (95% confidence intervals). The 30-day admit rate is the admission rate on the second visit to the ED. The discharged sample includes only adult patients discharged from the ED on the first visit. A segmented regression for the interrupted time series was used to estimate admission decision support in the face of ongoing secular trends in each of the metrics. Metric measures were regressed against time (month), adding an estimate for the acute change in level (change in the mean) from the end of the pre-implementation to the start of the post-implementation periods. Both the immediate effect (change in level in the regression line and linear trends (slopes, estimated as monthly trends) in metrics were estimated and compared pre- and post-implementation. NIHMS2160908-supplement-Appendix_B.docx (21.6KB, docx) Grant Research reported in this publication was partially supported by the National Center for Advancing Translational Sciences of the National Institutes of Health under Award Number UL1TR000130 (formerly by the National Center for Research Resources, Award Number UL1RR031986). Footnotes Meetings Poster presentation at SAEM 2015: Desai S, Gruber P, Voyageur C, Kim H, Terp S, Eiting E, Vasquez V: The Effect of Admission Decision Support Software on Emergency Department Operations. Acad Emerg Med 22 (S1): S386, 2015. Oral presentation at SAEM 2015: Gruber P, Desai S, Voyageur C, Kim H, Terp S, Eiting E, Vasquez V: Effects of Admission Decision Support Criteria on Emergency Department Disposition and Patient Re-visits. Acad Emerg Med 22 (S1): S81, 2015. None of the authors have a conflict of interest. References 1. Chang Y, Ketterlin R, Laiben G. The $6 Million Question: Can Process Improvement Ensure Appropriate Hospitalizations? J Healthcare Quality. 2008;30(2): 15–24. [ DOI ] [ PubMed ] [ Google Scholar ] 2. Centers for Medicare & Medicaid Services. Medicare Fee-for-Service 2013 Improper Payments Report. 2013. Available at: http://www.cms.gov/Research-Statistics-Data-and-Systems/Monitoring-Programs/Medicare-FFS-Compliance-Programs/CERT/CERT-Reports-Items/Downloads/MedicareFee-for-Service2013ImproperPaymentsReport.pdf . Accessed September 20, 2015. 3. McKendry MJ, Van Horn J. Today’s Hospital-based Case Manager: How One Hospital Integrated/Adopted Evidenced-based Medicine Using InterQual Criteria. Lippincott’s Case Manag. 2004;9(2): 61–71. [ DOI ] [ PubMed ] [ Google Scholar ] 4. Wickizer TM, Lessler D. Utilization Management: Issues, Effects, and Future Prospects. Ann Rev Public Health. 2002;23: 233–54. [ DOI ] [ PubMed ] [ Google Scholar ] 5. Mitus AJ. The Birth of InterQual: Evidence-based Decision Support Criteria That Helped Change Healthcare. Prof Case Manag 2008;13(4): 228–33. [ DOI ] [ PubMed ] [ Google Scholar ] 6. Gray BH, Field MJ. Institute of Medicine (US) Committee on Utilization Management by Third Parties: Controlling Costs and Changing Patient Care? The Role of Utilization Management. Washington DC: National Academies Press; 1989. [ PubMed ] [ Google Scholar ] 7. Interqual ® Level of Care Criteria [computer program]. Version 2014. San Francisco: McKesson; 2014. [ Google Scholar ] 8. Medical Expenditure Panel Survey [database online]. Table 5 (Hospital Inpatient Services: Median and Mean Expenses per Person with Expense and Distribution of Expenses by Source of Payment) & Table 6 (Emergency Room Services: Median and Mean Expenses per Person with Expense and Distribution of Expenses by Source of Payment). United States: Agency for Healthcare Research and Quality; 2014. [ Google Scholar ] 9. Department of Health and Human Services: Centers for Medicare and Medicaid Services. Medicare and Medicaid Programs: Hospital Outpatient Prospective Payment and Ambulatory Surgical Center Payment Systems and Quality Programs. Federal Register 2014; 79 (217): 66812. [ PubMed ] [ Google Scholar ] 10. Feldstein PJ, Wickizer TM, Wheeler JR. Private Cost Containment: The Effects of Utilization Review Programs on Health Care Use and Expenditures. NEJM 1988; 318(20): 1310–14. [ DOI ] [ PubMed ] [ Google Scholar ] 11. Wickizer TM, Wheeler JR, Feldstein PJ. Does Utilization Review Reduce Unnecessary Hospital Care and Contain Costs? Med Care 1989; 27(6): 632–47. [ DOI ] [ PubMed ] [ Google Scholar ] 12. Wickizer TM, Feldstein PJ, Wheeler JR, et al. Reducing Hospital Use and Expenditures Through Utilization Review: Findings from an Outcome Evaluation. Qual Assur Util Rev 1990;5(3):80–85. [ DOI ] [ PubMed ] [ Google Scholar ] 13. Murray ME, Darmody JV. Clinical and Fiscal Outcomes of Utilization Review. Outcomes Management 2004;81(1): 19–25. [ PubMed ] [ Google Scholar ] 14. Rosenberg SN, Allen DR, Handte JS, et al. Effect of Utilization Review in a Fee-for-Service Health Insurance Plan. NEJM 1995;333: 1326–1331. [ DOI ] [ PubMed ] [ Google Scholar ] 15. Milstein A. Managing Utilization Management: A Purchaser’s View. Health Aff 1997;16(3): 87–90. [ DOI ] [ PubMed ] [ Google Scholar ] 16. Wickizer TM, Lessler D, Boyd-Wickizer J. Effects of Health Care Cost-Containment Programs on Patterns of Care and Readmissions among Children and Adolescents. Am. J. Public Health 1999;89(9):1353–58. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 17. Lessler D, Wickizer T. The Impact of Utilization Management on Readmissions Among Patients with Cardiovascular Disease. Health Services Research 2000;34(6), 1315–1329. [ PMC free article ] [ PubMed ] [ Google Scholar ] 18. Wickizer TM, Lessler D. Do Treatment Restrictions Imposed by Utilization Management Increase the Likelihood of Readmission for Psychiatric Patients? Med Care 1998;36(6): 844–50. [ DOI ] [ PubMed ] [ Google Scholar ] 19. Schlesinger MJ, Gray BH, Perreira KM. Medical Professionalism under Managed Care: The Pros and Cons of Utilization Review. Health Aff 1997;16: 106–124. [ DOI ] [ PubMed ] [ Google Scholar ] 20. Murray ME, Henriques JB. An Exploratory Cost Analysis of Performing Hospital-Based Concurrent Utilization Review. Am J Manag Care 2003;9 (7): 512–518. [ PubMed ] [ Google Scholar ] 21. Wickizer TM, Wheeler JRC, Feldstein PJ. Have Hospital Inpatient Cost Containment Programs Contributed to the Growth in Outpatient Expenditures? Analysis of the Substitution Effect Associated with Hospitalization Utilization Review. Med Care 1991;29(5): 442–451. [ DOI ] [ PubMed ] [ Google Scholar ] Associated Data This section collects any data citations, data availability statements, or supplementary materials included in this article. Supplementary Materials Appendix A Appendix A: Data analysis flowchart, including initial number of ED visits, exclusions, and final number analyzed. NIHMS2160908-supplement-Appendix_A.pdf (26.4KB, pdf) Appendix B Appendix B: Throughput, disposition rates, and revisits within 30 days for all adult emergency department visits during the pre-implementation (9/11-7/12) and post-implementation (2/13-12/13) periods. Numbers in table are regression estimates (95% confidence intervals). The 30-day admit rate is the admission rate on the second visit to the ED. The discharged sample includes only adult patients discharged from the ED on the first visit. A segmented regression for the interrupted time series was used to estimate admission decision support in the face of ongoing secular trends in each of the metrics. Metric measures were regressed against time (month), adding an estimate for the acute change in level (change in the mean) from the end of the pre-implementation to the start of the post-implementation periods. Both the immediate effect (change in level in the regression line and linear trends (slopes, estimated as monthly trends) in metrics were estimated and compared pre- and post-implementation. NIHMS2160908-supplement-Appendix_B.docx (21.6KB, docx) ACTIONS View on publisher site PDF (176.2 KB) Cite Collections Permalink RESOURCES Similar articles Cited by other articles Links to NCBI Databases Follow NCBI NCBI on X (formerly known as Twitter) NCBI on Facebook NCBI on LinkedIn NCBI on GitHub NCBI RSS feed Connect with NLM NLM on X (formerly known as Twitter) NLM on Facebook NLM on YouTube National Library of Medicine 8600 Rockville Pike Bethesda, MD 20894 Web Policies FOIA HHS Vulnerability Disclosure Help Accessibility Careers NLM NIH HHS USA.gov Back to Top

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