Managing prior approval for site-of-service referrals: an algorithmic approach.

OBJECTIVES: Many payers and health care providers are either currently using or considering use of prior authorization schemes to redirect patient care away from hospital outpatient departments toward free-standing ambulatory surgical centers owing to the payment differential between these facilities. In this work we work with a medium size payer to develop and lay out a process for analysis of claims data that allows payers to conservatively estimate potential savings from such policies based on their specific case mix and provider network. STUDY DESIGN: We analyzed payment information for a medium-sized managed care organization to identify movable cases that can reduce costs, estimate potential savings, and recommend implementation policy alternatives. METHODS: We analyze payment data, including all professional and institutional fees over a 15-month period. A rules-based algorithm was developed to identify episodes of care with at least one alternate site for each episode, and potential savings from a site-of-service policy. RESULTS: Data on 64,884 episodes of care were identified as possible instances that could be subject to the policy. Of those, 7,679 were found to be attractive candidates for movement. Total projected savings was approximately $8.2 million, or over $1,000 per case. CONCLUSIONS: Instituting a site-of-service policy can produce meaningful savings for small and medium payers. Tailoring the policy to the specific patient and provider population can increase the efficacy of such policies in comparison to policies previously established by other payers.

Applying JIT principles to resident education to reduce patient delays: a pilot study in an academic medical center pain clinic.

OBJECTIVES: This study investigated the effect on patient waiting times, patient/doctor contact times, flow times, and session completion times of having medical trainees and attending physicians review cases before the clinic session. The major hypothesis was that review of cases prior to clinic hours would reduce waiting times, flow times, and use of overtime, without reducing patient/doctor contact time. DESIGN: Prospective quality improvement. SETTING: Specialty pain clinic within Johns Hopkins Outpatient Center, Baltimore, MD, United States. PARTICIPANTS: Two attending physicians participated in the intervention. Processing times for 504 patient visits are involved over a total of 4 months. INTERVENTION: Trainees were assigned to cases the day before the patient visit. Trainees reviewed each case and discussed it with attending physicians before each clinic session. PRIMARY AND SECONDARY OUTCOME MEASURES: Primary measures were activity times before and after the intervention. These were compared and also used as inputs to a discrete event simulation to eliminate differences in the arrival process as a confounding factor. RESULTS: The average time that attending physicians spent teaching trainees while the patient waited was reduced, but patient/doctor contact time was not significantly affected. These changes reduced patient waiting times, flow times, and clinic session times. CONCLUSIONS: Moving some educational activities ahead of clinic time improves patient flows through the clinic and decreases congestion without reducing the times that trainees or patients interact with physicians.

Using process analysis to assess the impact of medical education on the delivery of pain services: a natural experiment.

BACKGROUND: The medical, social, and economic effects of the teaching mission on delivery of care at an academic medical center (AMC) are not fully understood. When a free-standing private practice ambulatory clinic with no teaching mission was merged into an AMC, a natural experiment was created. The authors compared process measures across the two settings to observe the differences in system performance introduced by the added steps and resources of the AMC's teaching mission. METHODS: After creating process maps based on activity times realized in both settings, the authors developed discrete-event simulations of the two environments. The two settings were comparable in the levels of key resources, but the AMC process flow included three residents/fellows. Simulation enabled the authors to consider an identical schedule across the two settings. RESULTS: Under identical schedules, the average accumulated processing time per patient was higher in the AMC. However, the use of residents allowed simultaneous processing of multiple patients. Consequently, the AMC had higher throughput (3.5 vs. 2.7 patients per hour), higher room utilization (82.2% vs. 75.5%), reduced utilization of the attending physician (79.0% vs. 93.4%), and a shorter average waiting time (30.0 vs. 83.9 min). In addition, the average completion time for the final patient scheduled was 97.9 min less, and the average number of patients treated before incurring overtime was 37.9% greater. CONCLUSIONS: Although the teaching mission of the AMC adds processing steps and costs, the use of trainees within the process serves to increase throughput while decreasing waiting times and the use of overtime.

Changes to physician processing times in response to clinic congestion and patient punctuality: a retrospective study.

OBJECTIVES: We examine interactions among 3 factors that affect patient waits and use of overtime in outpatient clinics: clinic congestion, patient punctuality and physician processing rates. We hypothesise that the first 2 factors affect physician processing rates, and this adaptive physician behaviour serves to reduce waiting times and the use of overtime. SETTING: 2 urban academic clinics and an affiliated suburban clinic in metropolitan Baltimore, Maryland, USA. PARTICIPANTS: Appointment times, patient arrival times, start of service and physician processing times were collected for 105 visits at a low-volume suburban clinic 1, 264 visits at a medium-volume academic clinic 2 and 22 266 visits at a high-volume academic clinic 3 over 3 distinct spans of time. INTERVENTION: Data from the first clinic were previously used to document an intervention to influence patient punctuality. This included a policy that tardy patients were rescheduled. PRIMARY AND SECONDARY OUTCOME MEASURES: Clinicians' processing times were gathered, conditioned on whether the patient or clinician was tardy to test the first hypothesis. Probability distributions of patient unpunctuality were developed preintervention and postintervention for the clinic in which the intervention took place and these data were used to seed a discrete-event simulation. RESULTS: Average physician processing times differ conditioned on tardiness at clinic 1 with p=0.03, at clinic 2 with p=10-5 and at clinic 3 with p=10-7. Within the simulation, the adaptive physician behaviour degrades system performance by increasing waiting times, probability of overtime and the average amount of overtime used. Each of these changes is significant at the p<0.01 level. CONCLUSIONS: Processing times differed for patients in different states in all 3 settings studied. When present, this can be verified using data commonly collected. Ignoring these behaviours leads to faulty conclusions about the efficacy of efforts to improve clinic flow.

Patient punctuality and clinic performance: observations from an academic-based private practice pain centre: a prospective quality improvement study.

OBJECTIVES: The aim of this study was to examine the effects of an intervention to alter patient unpunctuality. The major hypothesis was that the intervention will change the distribution of patient unpunctuality by decreasing patient tardiness and increasing patient earliness. DESIGN: Prospective Quality Improvement. SETTING: Specialty Pain Clinic in suburban Baltimore, Maryland, USA. PARTICIPANTS: The patient population ranged in age from 18 to 93 years. All patients presenting to the clinic during the study period were included in the study. The average monthly volume was 86.2 (SD=13) patients. A total of 1500 patient visits were included in this study. INTERVENTIONS: We tracked appointment times and patient arrival times at an ambulatory pain clinic. An intervention was made in which patients were informed that tardy patients would not be seen and would be rescheduled. This policy was enforced over a 12-month period. PRIMARY AND SECONDARY OUTCOME MEASURES: The distribution of patient unpunctuality was developed preintervention and at 12 months after implementation. Distribution parameters were used as inputs to a discrete event simulation to determine effects of the change in patient unpunctuality on clinic delay. RESULTS: Data regarding patient unpunctuality were gathered by direct observation before and after implementation of the intervention. The mean unpunctuality changed from -20.5 min (110 observations, SD=1.7) preintervention to -23.2 (169, 1.2) at 1 month after the intervention, -23.8 min (69, 1.8) at 6 months and -25.0 min (71, 1.2) after 1 year. The unpunctuality 12 months after initiation of the intervention was significantly different from that prior to the intervention (p<0.05). CONCLUSIONS: Physicians and staff are able to alter patient arrival patterns to reduce patient unpunctuality. Reducing tardiness improves some measures of clinic performance, but may not always improve waiting times. Accommodating early arriving patients does serve to improve clinic performance.