Improving outpatient clinic efficiency using computer simulation

A systematic literature review of operational research methods for modelling patient flow and outcomes within community healthcare and other settings

An ambition of healthcare policy has been to move more acute services into community settings. This systematic literature review presents analysis of published operational research methods for modelling patient flow within community healthcare, and for modelling the combination of patient flow and outcomes in all settings. Assessed for inclusion at three levels - with the references from included papers also assessed - 25 "Patient flow within community care", 23 "Patient flow and outcomes" papers and 5 papers within the intersection are included for review. Comparisons are made between each paper's setting, definition of states, factors considered to influence flow, output measures and implementation of results. Common complexities and characteristics of community service models are discussed with directions for future work suggested. We found that in developing patient flow models for community services that use outcomes, transplant waiting list may have transferable benefits.

Measuring the operational impact of digitized hospital records: a mixed methods study

Background

Digitized (scanned) medical records have been seen as a means for hospitals to reduce costs and improve access to records. However, clinical usability of digitized records can potentially have negative effects on productivity.

Methods

Data were collected during follow-up outpatient consultations in two NHS hospitals by non-clinical observers using a work sampling approach in which pre-defined categories of clinician time usage were specified. Quantitative data was analysed using two-way ANOVA models and the Mann-Whitney U test. A focus group was held with clinicians to qualitatively explore their experiences using digitized medical records. The quantitative and qualitative results were synthesized.

Results

Four hundred six consultations were observed. Using paper records, there was a significant difference in consultation times between hospitals (p = 0.016) and a significant difference in consultation times between specialties within hospitals (p = 0.003). Using digitized records there was a significant difference in consultation times between specialties within a hospital (p = 0.001). Excluding outliers, there was no significant difference between consultation times using digitized records compared with consultations using paper records in the same hospital, either at site (p > =0.285) or specialty level (p > =0.122). With digitized records at site A, two out of three specialties showed a significant increase in time spent searching computer records (p < =0.010, Δ = 01:50–07:10) and one specialty had a corresponding reduction in time spent searching paper records (p = 0.015, Δ = −00:28). Site B showed a notable increase in direct patient care (p < 0.001, Δ = 04:20–06:00) and time spent searching computer records (p < =0.043, Δ = 00:10–01:40) and reductions in the other time categories.

The focus group confirmed that the most recent clinical letter was a vital document in the patient record, often containing most of the required information. Concerns were expressed about consistency of scanning practice, causing uncertainty about what could be relied upon to exist in the digitized record. Benefits of digitized records included: access from multiple locations, better prepared ward rounds, improved inpatient handovers and an improved timeline of patient events. Limitations of digitized records included: increased complexity of creating a patient summary, display of specialised content such as hand-drawn diagrams, inability to quickly flick through the pages to find relevant content.

Conclusions

Digitized medical records can be implemented without detrimental operational impact. Inherent differences between specialties can outweigh the differences between paper and digitized records. Clear and consistent operational processes are vital for the reliability and usability of digitized medical records. Divergent views about usability (such as whether patient summary information is better or worse) may reflect familiarity with features of the digitized record.

Trialability, observability and risk reduction accelerating individual innovation adoption decisions

Purpose

– The purpose of this paper is to provide a retrospective analysis of computer simulation’s role in accelerating individual innovation adoption decisions. The process innovation examined is Lean Systems Thinking, and the organizational context is the imaging department of an Australian public hospital.

Design/methodology/approach

– Intrinsic case study methods including observation, interviews with radiology and emergency personnel about scheduling procedures, mapping patient appointment processes and document analysis were used over three years and then complemented with retrospective interviews with key hospital staff. The multiple data sources and methods were combined in a pragmatic and reflexive manner to explore an extreme case that provides potential to act as an instructive template for effective change.

Findings

– Computer simulation of process change ideas offered by staff to improve patient-flow accelerated the adoption of the process changes, largely because animated computer simulation permitted experimentation (trialability), provided observable predictions of change results (observability) and minimized perceived risk.

Research limitations/implications

– The difficulty of making accurate comparisons between time periods in a health care setting is acknowledged.

Practical implications

– This work has implications for policy, practice and theory, particularly for inducing the rapid diffusion of process innovations to address challenges facing health service organizations and national health systems.

Originality/value

– The research demonstrates the value of animated computer simulation in presenting the need for change, identifying options, and predicting change outcomes and is the first work to indicate the importance of trialability, observability and risk reduction in individual adoption decisions in health services.

Improving operating room productivity via parallel anesthesia processing

PURPOSE

Parallel processing of regional anesthesia may improve operating room (OR) efficiency in patients undergoes upper extremity surgical procedures. The purpose of this paper is to evaluate whether performing regional anesthesia outside the OR in parallel increases total cases per day, improve efficiency and productivity.

DESIGN/METHODOLOGY/APPROACH

Data from all adult patients who underwent regional anesthesia as their primary anesthetic for upper extremity surgery over a one-year period were used to develop a simulation model. The model evaluated pure operating modes of regional anesthesia performed within and outside the OR in a parallel manner. The scenarios were used to evaluate how many surgeries could be completed in a standard work day (555 minutes) and assuming a standard three cases per day, what was the predicted end-of-day time overtime.

FINDINGS

Modeling results show that parallel processing of regional anesthesia increases the average cases per day for all surgeons included in the study. The average increase was 0.42 surgeries per day. Where it was assumed that three cases per day would be performed by all surgeons, the days going to overtime was reduced by 43 percent with parallel block. The overtime with parallel anesthesia was also projected to be 40 minutes less per day per surgeon.

RESEARCH LIMITATIONS/IMPLICATIONS

Key limitations include the assumption that all cases used regional anesthesia in the comparisons. Many days may have both regional and general anesthesia. Also, as a case study, single-center research may limit generalizability.

PRACTICAL IMPLICATIONS

Perioperative care providers should consider parallel administration of regional anesthesia where there is a desire to increase daily upper extremity surgical case capacity. Where there are sufficient resources to do parallel anesthesia processing, efficiency and productivity can be significantly improved.

ORIGINALITY/VALUE

Simulation modeling can be an effective tool to show practice change effects at a system-wide level.

Improving patient waiting times: a simulation study of an obesity care service

Background

Obesity care services are often faced with the need to adapt their resources to rising levels of demand. The main focus of this study was to help prioritise planned investments in new capacity allowing the service to improve patient experience and meet future anticipated demand.

Methods

We developed computer models of patient flows in an obesity service in an Academic Health Science Centre that provides lifestyle, pharmacotherapy and surgery treatment options for the UK's National Health Service. Using these models we experiment with different scenarios to investigate the likely impact of alternative resource configurations on patient waiting times.

Results

Simulation results show that the timing and combination of adding extra resources (eg, surgeons and physicians) to the service are important. For example, increasing the capacity of the pharmacotherapy clinics equivalent to adding one physician reduced the relevant waiting list size and waiting times, but it then led to increased waiting times for surgical patients. Better service levels were achieved when the service operates with the resource capacity of two physicians and three surgeons. The results obtained from this study had an impact on the planning and organisation of the obesity service.

Conclusions

Resource configuration combined with demand management (reduction in referral rates) along the care service can help improve patient waiting time targets for obesity services, such as the 18 week target of UK's National Health Service. The use of simulation models can help stakeholders understand the interconnectedness of the multiple microsystems (eg, clinics) comprising a complex clinical service for the same patient population, therefore, making stakeholders aware of the likely impact of resourcing decisions on the different microsystems.

Hospital waiting time: the forgotten premise of healthcare service delivery?

PURPOSE

This is a national study which aims to determine the average waiting time in Malaysian public hospitals and to gauge the level of patient satisfaction with the waiting time. It also aims to identify factors perceived by healthcare providers which contribute to the waiting time problem.

DESIGN/METHODOLOGY/APPROACH

Self-administered questionnaires were the main method of data collection. Two sets of questionnaires were used. The first set solicited information from patients on their waiting time expereince. The second set elucidated information from hospital employees on the possible causes of lengthy waiting time. The questionnaires were administered in 21 public hospitals throughout all 13 states in Malaysia. A total of 13,000 responses were analysed for the patient survey and almost 3,000 were analysed for the employee survey.

FINDINGS

The findings indicate that on average, patients wait for more than two hours from registration to getting the prescription slip, while the contact time with medical personnel is only on average 15 minutes. Employee surveys on factors contributing to the lengthy waiting time indicate employee attitude and work process, heavy workload, management and supervision problems, and inadequate facilities to be among the contributory factors to the waiting time problem.

SOCIAL IMPLICATIONS

Public healthcare in Malaysia is in a state of "excess demand", where demand for subsidised healthcare far outstrips supply, due to the large fee differential between public and private healthcare services. There is a need for hospital managers to reduce the boredom faced by patients while waiting, and to address the waiting time problem in a more scientific manner, as has been carried out in other countries through simulation and modelling techniques.

ORIGINALITY/VALUE

Healthcare organisations are keen to address their waiting time problem. However, not much research has been carried out in this area. The study thus fills the lacuna in waiting time studies in healthcare organisations.

Managing health care decisions and improvement through simulation modeling

Simulation modeling is a way to test changes in a computerized environment to give ideas for improvements before implementation. This article reviews research literature on simulation modeling as support for health care decision making. The aim is to investigate the experience and potential value of such decision support and quality of articles retrieved. A literature search was conducted, and the selection criteria yielded 59 articles derived from diverse applications and methods. Most met the stated research-quality criteria. This review identified how simulation can facilitate decision making and that it may induce learning. Furthermore, simulation offers immediate feedback about proposed changes, allows analysis of scenarios, and promotes communication on building a shared system view and understanding of how a complex system works. However, only 14 of the 59 articles reported on implementation experiences, including how decision making was supported. On the basis of these articles, we proposed steps essential for the success of simulation projects, not just in the computer, but also in clinical reality. We also presented a novel concept combining simulation modeling with the established plan-do-study-act cycle for improvement. Future scientific inquiries concerning implementation, impact, and the value for health care management are needed to realize the full potential of simulation modeling.

Factors Relating to Patient Visit Time With a Physician

This study sought to identify factors that increase or decrease patient time with a physician, determine which combinations of factors are associated with the shortest and longest visits to physicians, quantify how much physicians contribute to variation in the time they spend with patients, and assess how well patient time with a physician can be predicted. Data were acquired from a modified replication of the 1997–1998 National Ambulatory Medical Care Survey, administered by the Kentucky Ambulatory Network to 56 primary care clinicians at 24 practice sites in 2001 and 2002. A regression tree and a linear mixed model (LMM) were used to discover multivariate associations between patient time with a physician and 22 potentially predictive factors. Patient time with a physician was related to the number of diagnoses, whether non-illness care was received, and whether the patient had been seen before by the physician or someone at the practice. Approximately 38% of the variation in patient time with a physician was accounted for by predictive factors in the tree; roughly 33% was explained by predictive factors in the LMM, with another 12% linked to physicians. Knowledge of patient characteristics and needs could be used to schedule office visits, potentially improving patient flow through a clinic and reducing waiting times.

Improving clinical access and continuity through physician panel redesign

BACKGROUND

Population growth, an aging population and the increasing prevalence of chronic disease are projected to increase demand for primary care services in the United States.

OBJECTIVE

Using systems engineering methods, to re-design physician patient panels targeting optimal access and continuity of care.

DESIGN

We use computer simulation methods to design physician panels and model a practice's appointment system and capacity to provide clinical service. Baseline data were derived from a primary care group practice of 39 physicians with over 20,000 patients at the Mayo Clinic in Rochester, MN, for the years 2004-2006. Panel design specifically took into account panel size and case mix (based on age and gender).

MEASURES

The primary outcome measures were patient waiting time and patient/clinician continuity. Continuity is defined as the inverse of the proportion of times patients are redirected to see a provider other than their primary care physician (PCP).

RESULTS

The optimized panel design decreases waiting time by 44% and increases continuity by 40% over baseline. The new panel design provides shorter waiting time and higher continuity over a wide range of practice panel sizes.

CONCLUSIONS

Redesigning primary care physician panels can improve access to and continuity of care for patients.

Analysis of factors causing long patient waiting time and clinic overtime in outpatient clinics

This paper is focused on the factors causing long patient waiting time/clinic overtime in outpatient clinics and how to mitigate them using discrete event simulation. A two-week period of data collection is conducted in an outpatient clinic of a Singapore government hospital. Detailed time study from patient arrival to patient departure is conducted, and the possible factors causing long patient waiting time/clinic overtime are discussed. A discrete simulation model is constructed to illustrate how to improve the clinic performance by mitigating the detected factors. Simulation and implementation results show that significant improvement is achieved if the factors are well addressed.

Improving the efficiency of physical examination services

The objective of our project was to improve the efficiency of the physical examination screening service of a large hospital system. We began with a detailed simulation model to explore the relationships between four performance measures and three decision factors. We then attempted to identify the optimal physician inquiry starting time by solving a goal-programming problem, where the objective function includes multiple goals. One of our simulation results shows that the proposed optimal physician inquiry starting time decreased patient wait times by 50% without increasing overall physician utilization.

Using computer simulation to reduce access time for outpatient departments

AIM

To develop general applicable models for analysing the capacity needed in appointment-based hospital facilities.

METHOD

A fairly simple analytical queuing model was used to obtain rapid global insight into the capacity needed to meet the norm of seeing 95% of all new patients within 2 weeks. For more detailed analysis, a simulation model was developed that could handle daily variations in demand and capacity schedules. The capacity needed to eliminate backlogs and the capacity needed to keep access time within 2 weeks was calculated. Both models were applied to two outpatient departments (neurology and gynaecology) at the Academic Medical Center in Amsterdam, the Netherlands. Model results for neurology were implemented.

RESULTS

For neurology, to eliminate the 6-week backlog, 26 extra consultations per week were needed over 2 months. A permanent increase of 2-weekly consultations was required to keep access time within 2 weeks. Evaluation after implementation showed the improvements the model had predicted. The gynaecology department had sufficient capacity. With the simulation, it was calculated that the same service level could be achieved with 14% less capacity. Thus the models supported decisions made for departments with shortages of capacity as well as those for departments with adequate capacity.

CONCLUSION

The analytical model provided quick insight into the extra capacity needed for the neurology department. The added value of the simulation model was the possibility of taking into account variations in demand for different weekdays and a realistic schedule for doctors' consultations. General applicability of the models was shown by applying both models to the gynaecology department.

Collection and preparation of sensor network data to support modeling and analysis of outpatient clinics

Simulation studies of outpatient clinics often involve significant data collection challenges. We describe an approach for data collection using sensor networks which facilitates the collection of a large volume of very detailed patient flow data through healthcare clinics. Such data requires extensive preprocessing before it is ready for analysis. We present a general data preparation framework for sensor network generated data with particular emphasis on the creation and analysis of patient path strings. Several examples of the analysis of sensor network data are also presented. Our approach has been used in two large outpatient clinics in the United States.

Development and assessment of a priority score for cataract surgery

BACKGROUND

Point-count measures of clinical priority are increasingly put forward for managing waiting lists. However, their development does not consider explicitly the appropriateness of the indications. Furthermore, an estimate of their effect in clinical practice is needed, assessing the amount of gains and losses in terms of time waited for patients with different priority scores.

METHODS

We developed appropriateness criteria for cataract surgery using the RAND method and applied them to a sample of 567 patients consecutively placed on a waiting list for cataract surgery. In addition, clinicians were asked to express the priority attributed to each patient using a 10-cm visual analogue scale, where 0 = minimal priority and 10 = maximum priority. We developed a priority score, using regression analysis to identify the set of clinical characteristics that best predicted clinicians' priority rating and to estimate their individual weight. We used a computer simulation model to compare mean waiting times with management of the waiting list using the priority score and using the "first-come, first-served" approach.

RESULTS

Overall, 332 patients (60.8%) were referred for cataract surgery for indications deemed appropriate, and their mean priority rating was 5.9 (95% confidence interval [CI] 5.7-6.1). The corresponding figures for the 201 (36.8%) uncertain indications and the 13 (2.4%) inappropriate indications were 4.5 (95% CI 4.1-4.7) and 2.6 (95% CI 1.3-3.9) respectively. The clinical characteristics included in the priority score (visual acuity in the operated eye and in the contralateral eye, visual function and ability to live or work independently) accounted for 35% of the variance in clinicians' ratings of priority. In the computer simulation model, patients with the highest priority experienced a variable reduction in mean waiting time (9% to 27%) depending to how time spent waiting was integrated into the clinical score.

INTERPRETATION

We conclude that the use of priority ratings in the management of a waiting list for cataract surgery leads to results that maintain the desirable coherence between priority and appropriateness of indication. The results also suggest that the implementation in clinical practice of priority scores may be worth the effort, given the potential reduction in waiting time for patients at high priority.

Managing a mixed-registration-type appointment system in outpatient clinics

INTRODUCTION

Improving outpatient resource utilization significantly enhances the efficiency of healthcare organizations. Substantial number of walk-in patients (average of 72% in our study) to outpatient services is a universal characteristic of Taiwan's healthcare organizations. Consequently, scheduling becomes extremely complicated and important. Selecting the right scheduling alternative, a healthcare organization can markedly improve operating efficiency of outpatient resources.

OBJECTIVE

This research applied simulation methodology to analyze several scheduling solutions and found that setting the appropriate arrival time interval for preregistered patients significantly impacts queuing problems in outpatient services.

METHOD

Using established simulation models, the effects of various scheduling policies on patients' throughput time and waiting times were revealed. Under alternative model A, the first 20 numbers are reserved for scheduled patients; after that, only even numbers are offered for scheduled ones. Odd numbers after 20 are left for walk-ins. Under alternative model B, front numbers were assigned to scheduled patients successively. The later numbers were left for walk-ins. Alternative model C assigned scheduled patients with even numbers and walk-ins with odd numbers in sequence. Finally, alternative model D was designed to examine the optimal scheduled time interval by conducting the model with different scheduled time intervals such as 3, 5, 7, 9, and 11 min.

RESULT

The alternative sequence (alternative model C-assigning even numbers for scheduled patients and odd numbers for walk-in patients, or vice versa) significantly has the least throughput time (average: 34.9 min vs 55.2, 56.2, and 46.2 min) and waiting times (average: 14.7 min vs 34.9, 35.8, and 25.8 min) for walk-in patients compared with other registration strategies. Scheduling the appointments with flexible time interval (alternative model D) has the least throughput time (average: 24.2 min vs 28.4, 28.2, and 37.2 min) and waiting times (average: 8.0 min vs 12.5, 12.3, and 20.5 min) for scheduled patients compared with other registration strategies.

CONCLUSION

The findings of this research could be applied possibly to any outpatient clinic with mixed-registration-type (walk-in and scheduled), particularly which accounts for high percentage of walk-in patients.

Optimizing management and financial performance of the teaching ambulatory care clinic

OBJECTIVE

To examine how to optimize teaching ambulatory care clinics performance with regard to access to care, access to teaching, and financial viability.

DESIGN

Optimization analysis using computer simulation.

METHODS

A discrete-event simulation model of the teaching ambulatory clinic setting was developed. This method captures flow time, waiting time, competition for resources, and the interdependency of events, providing insight into system dynamics. Sensitivity analyses were performed on staffing levels, room availability, patient characteristics such as "new" versus "established" status, and clinical complexity and pertinent probabilities.

MAIN RESULTS

In the base-case, 4 trainees:preceptor, patient flow time (registration to check out) was 148 minutes (SD 5), wait time was 20.6 minutes (SD 4.4), the wait for precepting was 6.2 minutes (SD 1.2), and average daily net clinic income was $1,413. Utilization rates were preceptors (59%), trainees (61%), medical assistants (64%), and room (68%). Flow time and the wait times remained relatively constant for strategies with trainee:preceptor ratios <4:1 but increased with number of trainees steadily thereafter. Maximum revenue occurred with 3 preceptors and 5 trainees per preceptor. The model was relatively insensitive to the proportion of patients presenting who were new, and relatively sensitive to average evaluation and management (E/M) level. Flow and wait times rose on average by 0.05 minutes and 0.01 minutes per percent new patient, respectively. For each increase in average E/M level, flow time increased 8.4 minutes, wait time 1.2 minutes, wait for precepting 0.8 minutes, and net income increased by $490.

CONCLUSION

Teaching ambulatory care clinics appear to operate optimally, minimizing flow time and waiting time while maximizing revenue, with trainee-to-preceptor ratios between 3 and 7 to 1.