Data-Driven Scheduling for Improving Patient Efficiency in Ophthalmology Clinics

Factors influencing treatment and time spent with physicians in patients with uveitis compared to other ophthalmology subspecialties in the National Ambulatory Medical Care Survey

BACKGROUND

Cases of uveitis can necessitate long-term treatment resulting in recurrent follow-up appointments. Analysing the demographic distribution and patient factors influencing treatment and time spent with physicians in this population compared to other subspecialties of ophthalmology using the National Ambulatory Medical Care Survey (NAMCS) has not previously been studied.

METHODS

Data were extracted from the NAMCS database, a large, nationally representative survey of office-based specialists, entered between 2012-2016 and 2018. Demographics, time with physician, and payor types were compared between patients with a uveitis-related diagnosis codes versus all other ophthalmic subspecialty diagnoses.

RESULTS

Overall, 12,870 ophthalmic patients were included of which 300 had uveitis-related diagnosis codes. Uveitis patients were more likely to be non-Caucasian (p < 0.0001 to p = 0.022), visiting the physician's office due to flare of or treatment for a chronic medical problem (p < 0.0001 to p = 0.022). Adjusted for age, sex, race, and ethnicity, uveitis patients spent a significantly longer time (mean 27.5 min) compared to comprehensive ophthalmology patients (mean 25.5 min) with their physician (p = 0.0041). Among the uveitis patient population, African American patients (p = 0.0053), Hispanic or Latino (p = 0.034), and Medicaid (p = 0.035) patients had increased office visit times.

CONCLUSIONS

Those with uveitis spent more time with the physician than comprehensive patients. Race, ethnicity, payor type, and the major reason for the visit all significantly impacted uveitis office visit times. In order to manage their schedules, providers should be aware of the additional support and time needed by these patients during office visits.

Large language models and their impact in ophthalmology

The advent of generative artificial intelligence and large language models has ushered in transformative applications within medicine. Specifically in ophthalmology, large language models offer unique opportunities to revolutionise digital eye care, address clinical workflow inefficiencies, and enhance patient experiences across diverse global eye care landscapes. Yet alongside these prospects lie tangible and ethical challenges, encompassing data privacy, security, and the intricacies of embedding large language models into clinical routines. This Viewpoint highlights the promising applications of large language models in ophthalmology, while weighing up the practical and ethical barriers towards their real-world implementation. This Viewpoint seeks to stimulate broader discourse on the potential of large language models in ophthalmology and to galvanise both clinicians and researchers into tackling the prevailing challenges and optimising the benefits of large language models while curtailing the associated risks.

Optometrists' attitudes toward using OCT angiography lag behind other retinal imaging types

PURPOSE

While optometrists' attitudes toward established retinal imaging types are generally positive, they are unknown for optical coherence tomography angiography (OCTA). We performed a cross-sectional survey to estimate attitudes toward OCTA and identify clinician and/or practice characteristics that influence them.

METHODS

A paper-based survey was mailed to 252 randomly selected optometrists in Australia. Five-point Likert-scale items from a previous survey assessing attitudes toward new technology were included to probe respondent characteristics and attitudes toward retinal imaging. Performance expectancy attitudes toward OCTA were elicited by the statement 'I believe OCTA is useful in daily practice'. Mean scores out of five (mean [SD]) were rounded and mapped to appropriate descriptive statements.

RESULTS

The response rate was 47% (118/252). The mean (SD) age of respondents was 44.0 (13.8) years and 50.8% (60/118) were female. Optometrists had 19.9 (14.0) years of clinical experience and 66.9% (79/118) worked at independent practices. In total, 8.5% (10/118) of respondents used OCTA to provide clinical care. Optometrists agreed that optical coherence tomography (OCT), colour fundus imaging, ultra-wide field imaging and fundus autofluorescence (mean scores 3.6-4.7 out of 5) were useful in daily practice but felt neutral about whether OCTA was useful (3.4 [0.8]). Optometrists believed that OCTA was less enjoyable to use (p < 0.0001), less endorsed by peers (p < 0.0001) and felt less confident that they had the knowledge to interpret OCTA (p < 0.0001) compared to other retinal imaging types.

CONCLUSIONS

Optometrists are undecided on whether OCTA is useful in daily practice and had lower expectations that using OCTA would confer job performance benefits compared to other retinal imaging types. Further work is needed to advocate the benefits of using OCTA across the profession.

An operations research approach to automated patient scheduling for eye care using a multi-criteria decision support tool

Inefficient management of resources and waiting lists for high-risk ophthalmology patients can contribute to sight loss. The aim was to develop a decision support tool which determines an optimal patient schedule for ophthalmology patients. Our approach considers available booking slots as well as patient-specific factors. Using standard software (Microsoft Excel and OpenSolver), an operations research approach was used to formulate a mathematical model. Given a set of patients and clinic capacities, the model objective was to schedule patients efficiently depending on eyecare measure risk factors, referral-to-treatment times and targets, patient locations and slot availabilities over a pre-defined planning horizon. Our decision support tool can feedback whether or not a patient is scheduled. If a patient is scheduled, the tool determines the optimal date and location to book the patients' appointments, with a score provided to show the associated value of the decisions made. Our dataset from 519 patients showed optimal prioritization based on location, risk of serious vision loss/damage and the referral-to-treatment time. Given the constraints of available slots, managers can input hospital-specific parameters such as demand and capacity into our model. The model can be applied and implemented immediately, without the need for additional software, to generate an optimized patient schedule.

Appointment Scheduling Problem in Complexity Systems of the Healthcare Services: A Comprehensive Review

This paper provides a comprehensive review of Appointment Scheduling (AS) in healthcare service while we propose appointment scheduling problems and various applications and solution approaches in healthcare systems. For this purpose, more than 150 scientific papers are critically reviewed. The literature and the articles are categorized based on several problem specifications, i.e., the flow of patients, patient preferences, and random arrival time and service. Several methods have been proposed to shorten the patient waiting time resulting in the shortest idle times in healthcare centers. Among existing modeling such as simulation models, mathematical optimization techniques, Markov chain, and artificial intelligence are the most practical approaches to optimizing or improving patient satisfaction in healthcare centers. In this study, various criteria are selected for structuring the recent literature dealing with outpatient scheduling problems at the strategic, tactical, or operational levels. Based on the review papers, some new overviews, problem settings, and hybrid modeling approaches are highlighted.

Measuring Telehealth Visit Length and Schedule Adherence Using Videoconferencing Data

Background: The ability to measure clinical visit length is critical for operational efficiency, patient experience, and accurate billing. Despite the unprecedented surge in telehealth use in 2020, studies on visit length and schedule adherence in the telehealth setting are nonexistent in the literature. This article aims to demonstrate the use of videoconferencing data to measure telehealth visit length and schedule adherence. Materials and Methods: We used data from telehealth video visits at four clinical specialties at Nationwide Children's Hospital, including behavioral health (BH), speech pathology (SP), physical therapy/occupational therapy (PT/OT), and primary care (PC). We combined videoconferencing timestamp data with visit scheduling data to calculate the total visit length, examination length, and patient wait times. We also assessed schedule adherence, including patient on-time performance, examination on-time performance, provider schedule deviations, and schedule length deviations. Results: The analyses included a total of 175,876 telehealth video visits. On average, children with BH appointments spent a total of 57.2 min for each visit, followed by PT/OT (50.8 min), SP (42.1 min), and PC (25.0 min). The average patient wait times were 4.1 min (BH), 2.7 min (PT/OT), 2.8 min (SP), and 3.1 min (PC). The average examination lengths were 48.8 min (BH), 44.5 min (PT/OT), 34.9 min (SP), and 16.6 min (PC). Regardless of clinical specialty, actual examination lengths of most visits were shorter than the scheduled lengths, except that appointments scheduled for 15 min tended to run overtime. Conclusions: Videoconferencing data provide a low-cost, accurate, and readily available resource for measuring telehealth visit length and schedule adherence.

Automatic Recognition of Ocular Surface Diseases on Smartphone Images Using Densely Connected Convolutional Networks

Ocular surface disorder is one of common and prevalence eye diseases and complex to be recognized accurately. This work presents automatic classification of ocular surface disorders in accordance with densely connected convolutional networks and smartphone imaging. We use various smartphone cameras to collect clinical images that contain normal and abnormal, and modify end-to-end densely connected convolutional networks that use a hybrid unit to learn more diverse features, significantly reducing the network depth, the total number of parameters and the float calculation. The validation results demonstrate that our proposed method provides a promising and effective strategy to accurately screen ocular surface disorders. In particular, our deeply learned smartphone photographs based classification method achieved an average automatic recognition accuracy of 90.6%, while it is conveniently used by patients and integrated into smartphone applications for automatic patient-self screening ocular surface diseases without seeing a doctor in person in a hospital.

SUCCESSFUL INTERVENTIONS TO IMPROVE EFFICIENCY AND REDUCE PATIENT VISIT DURATION IN A RETINA PRACTICE

PURPOSE

To reduce the total clinic visit duration among retina providers in an academic ophthalmology department.

METHODS

All patient encounters across all providers in the department were analyzed to determine baseline clinic visit duration time, defined as the elapsed time between appointment time and checkout. To increase photography capacity, a major bottleneck identified through root cause analysis, four interventions were implemented: training ophthalmic technicians to perform fundus photography in addition to optical coherence tomographies, relocating photography equipment to be adjacent to examination rooms, procuring three additional Optos widefield retinal photography units, and shifting staff schedules to better align with that of the providers. These interventions were implemented in the clinics of two retina providers.

RESULTS

The average baseline visit duration for all patients across all providers was 87 minutes (19,550 patient visits). The previous average visit duration was 80 minutes for Provider 1 (557 patient visits) and 81 minutes for Provider 2 (1,246 patient visits). In the 4 weeks after interventions were implemented, the average visit duration decreased to 60 minutes for Provider 1 and 57 minutes for Provider 2.

CONCLUSION

A systematic approach and a multidisciplinary team resulted in targeted, cost-effective interventions that reduced total visit durations.

Measuring Electronic Health Record Use in the Pediatric ICU Using Audit-Logs and Screen Recordings

BACKGROUND

 Time spent in the electronic health record (EHR) has been identified as an important unit of measure for health care provider clinical activity. The lack of validation of audit-log based inpatient EHR time may have resulted in underuse of this data in studies focusing on inpatient patient outcomes, provider efficiency, provider satisfaction, etc. This has also led to a dearth of clinically relevant EHR usage metrics consistent with inpatient provider clinical activity.

OBJECTIVE

 The aim of our study was to validate audit-log based EHR times using observed EHR-times extracted from screen recordings of EHR usage in the inpatient setting.

METHODS

 This study was conducted in a 36-bed pediatric intensive care unit (PICU) at Lucile Packard Children's Hospital Stanford between June 11 and July 14, 2020. Attending physicians, fellow physicians, hospitalists, and advanced practice providers with ≥0.5 full-time equivalent (FTE) for the prior four consecutive weeks and at least one EHR session recording were included in the study. Citrix session recording player was used to retrospectively review EHR session recordings that were captured as the provider interacted with the EHR.

RESULTS

 EHR use patterns varied by provider type. Audit-log based total EHR time correlated strongly with both observed total EHR time (r = 0.98, p < 0.001) and observed active EHR time (r = 0.95, p < 0.001). Each minute of audit-log based total EHR time corresponded to 0.95 (0.87-1.02) minutes of observed total EHR time and 0.75 (0.67-0.83) minutes of observed active EHR time. Results were similar when stratified by provider role.

CONCLUSION

 Our study found inpatient audit-log based EHR time to correlate strongly with observed EHR time among pediatric critical care providers. These findings support the use of audit-log based EHR-time as a surrogate measure for inpatient provider EHR use, providing an opportunity for researchers and other stakeholders to leverage EHR audit-log data in measuring clinical activity and tracking outcomes of workflow improvement efforts longitudinally and across provider groups.

Distribution and determinants of hospital efficiency and relative productivity in county-level hospitals in rural China: an observational study

BACKGROUND

Cataract surgery is very important to prevent blindness, but its productivity and efficiency in China are unknown. Our study aimed to evaluate the geographical distribution of cataract surgeons and prospectively identify the factors associated with the increased productivity in cataract surgery and efficiency in outpatient ophthalmic services in rural Chinese hospitals.

METHODS

Data were prospectively collated from various hospital datasets and the census registered by the geographical unit county. Prior to mapping, the geographical location data of counties were cross-linked with the equivalent ophthalmologist and service output data to create categories and map multiple data attributes. Descriptive statistical analyses were performed to characterise the data stratified by county. Linear regression analyses were used to explore the factors associated with the increased productivity/efficiency.

RESULTS

The ophthalmologists, surgical productivity of ophthalmologists and outpatient efficacy of ophthalmologists significantly varied across counties. During the period between 2016 and 2018, the median (IQR) change in surgical productivity of and outpatient efficacy of ophthalmologists were 31.627 (-3.33 to 29.94) and 118.08 (-132.30 to 740.89). In the simple regression analysis for predictors of a high productivity change, only the increased number of phaco machine had statistical significance (p=0.003). In addition, only the gross domestic product per capita in 2016 was associated with an increased improvement in efficiency of outpatient services (p=0.008).

CONCLUSIONS

This study demonstrated that the ophthalmologist productivity and the efficiency of outpatient services were unequally geographically distributed, and their predictors were identified. Further studies to elucidate the extent of the problem and improve the health service delivery models are required.

Modeling the impact of COVID-19 on Retina Clinic Performance

BACKGROUND

COVID-19, a highly contagious respiratory virus, presents unique challenges to ophthalmology practice as a high-volume, office-based specialty. In response to the COVID-19 pandemic, many operational changes were adopted in our ophthalmology clinic to enhance patient and provider safety while maintaining necessary clinical operations. The aim of this study was to evaluate how measures adopted during the pandemic period affected retina clinic performance and patient satisfaction, and to model future clinic flow to predict operational performance under conditions of increasing patient and provider volumes.

METHODS

Clinic event timestamps and demographics were extracted from the electronic medical records of in-person retina encounters from March 15 to May 15, 2020 and compared with the same period in 2019 to assess patient flow through the clinical encounter. Patient satisfaction was evaluated by Press Ganey patient experience surveys obtained from randomly selected outpatient encounters. A discrete-events simulation was designed to model the clinic with COVID-era restrictions to assess operational performance under conditions of increasing patient and provider volumes.

RESULTS

Retina clinic volume declined by 62 % during the COVID-19 health emergency. Average check-in-to-technician time declined 79 %, total visit length declined by 46 %, and time in the provider phase of care declined 53 %. Patient satisfaction regarding access nearly doubled during the COVID-period compared with the prior year (p < 0.0001), while satisfaction with overall care and safety remained high during both periods. A model incorporating COVID-related changes demonstrated that wait time before rooming reached levels similar to the pre-COVID era by 30 patients-per-provider in a 1-provider model and 25 patients-per-provider in a 2-provider model (p < 0.001). Capacity to maintain distancing between patients was exceeded only in the two 2-provider model above 25 patients-per-provider.

CONCLUSIONS

Clinic throughput was optimized in response to the COVID-19 health emergency. Modeling these clinic changes can help plan for eventual volume increases in the setting of limits imposed in the COVID-era.

Artificial intelligence-assisted reduction in patients' waiting time for outpatient process: a retrospective cohort study

Background

Many studies suggest that patient satisfaction is significantly negatively correlated with the waiting time. A well-designed healthcare system should not keep patients waiting too long for an appointment and consultation. However, in China, patients spend notable time waiting, and the actual time spent on diagnosis and treatment in the consulting room is comparatively less.

Methods

We developed an artificial intelligence (AI)-assisted module and name it XIAO YI. It could help outpatients automatically order imaging examinations or laboratory tests based on their chief complaints. Thus, outpatients could get examined or tested before they went to see the doctor. People who saw the doctor in the traditional way were allocated to the conventional group, and those who used XIAO YI were assigned to the AI-assisted group. We conducted a retrospective cohort study from August 1, 2019 to January 31, 2020. Propensity score matching was used to balance the confounding factor between the two groups. And waiting time was defined as the time from registration to preparation for laboratory tests or imaging examinations. The total cost included the registration fee, test fee, examination fee, and drug fee. We used Wilcoxon rank-sum test to compare the differences in time and cost. The statistical significance level was set at 0.05 for two sides.

Results

Twelve thousand and three hundred forty-two visits were recruited, consisting of 6171 visits in the conventional group and 6171 visits in the AI-assisted group. The median waiting time was 0.38 (interquartile range: 0.20, 1.33) hours for the AI-assisted group compared with 1.97 (0.76, 3.48) hours for the conventional group (p < 0.05). The total cost was 335.97 (interquartile range: 244.80, 437.60) CNY (Chinese Yuan) for the AI-assisted group and 364.58 (249.70, 497.76) CNY for the conventional group (p < 0.05).

Conclusions

Using XIAO YI can significantly reduce the waiting time of patients, and thus, improve the outpatient service process of hospitals.

Using electronic health record audit logs to study clinical activity: a systematic review of aims, measures, and methods

OBJECTIVE

To systematically review published literature and identify consistency and variation in the aims, measures, and methods of studies using electronic health record (EHR) audit logs to observe clinical activities.

MATERIALS AND METHODS

In July 2019, we searched PubMed for articles using EHR audit logs to study clinical activities. We coded and clustered the aims, measures, and methods of each article into recurring categories. We likewise extracted and summarized the methods used to validate measures derived from audit logs and limitations discussed of using audit logs for research.

RESULTS

Eighty-five articles met inclusion criteria. Study aims included examining EHR use, care team dynamics, and clinical workflows. Studies employed 6 key audit log measures: counts of actions captured by audit logs (eg, problem list viewed), counts of higher-level activities imputed by researchers (eg, chart review), activity durations, activity sequences, activity clusters, and EHR user networks. Methods used to preprocess audit logs varied, including how authors filtered extraneous actions, mapped actions to higher-level activities, and interpreted repeated actions or gaps in activity. Nineteen studies validated results (22%), but only 9 (11%) through direct observation, demonstrating varying levels of measure accuracy.

DISCUSSION

While originally designed to aid access control, EHR audit logs have been used to observe diverse clinical activities. However, most studies lack sufficient discussion of measure definition, calculation, and validation to support replication, comparison, and cross-study synthesis.

CONCLUSION

EHR audit logs have potential to scale observational research but the complexity of audit log measures necessitates greater methodological transparency and validated standards.

Measuring Primary Care Exam Length Using Electronic Health Record Data

BACKGROUND

Physicians' time with patients is a critical input to care, but is typically measured retrospectively through survey instruments. Data collected through the use of electronic health records (EHRs) offer an alternative way to measure visit length.

OBJECTIVE

To measure how much time primary care physicians spend with their patients, during each visit.

RESEARCH DESIGN

We used a national source of EHR data for primary care practices, from a large health information technology company. We calculated exam length and schedule deviations based on timestamps recorded by the EHR, after implementing sequential data refinements to account for non-real-time EHR use and clinical multitasking. Observational analyses calculated and plotted the mean, median, and interquartile range of exam length and exam length relative to scheduled visit length.

SUBJECTS

A total of 21,010,780 primary care visits in 2017.

MEASURES

We identified primary care visits based on physician specialty. For these visits, we extracted timestamps for EHR activity during the exam. We also extracted scheduled visit length from the EHR's practice management functionality.

RESULTS

After data refinements, the average primary care exam was 18.0 minutes long (SD=13.5 min). On average, exams ran later than their scheduled duration by 1.2 minutes (SD=13.5 min). Visits scheduled for 10 or 15 minutes were more likely to exceed their allotted time than visits scheduled for 20 or 30 minutes.

CONCLUSIONS

Time-stamped EHR data offer researchers and health systems an opportunity to measure exam length and other objects of interest related to time.

Evaluation of Electronic Health Record Implementation in an Academic Oculoplastics Practice

PURPOSE

Despite increasing electronic health record (EHR) adoption, perceptions of EHRs are negative among ophthalmologists due to concerns about productivity, costs, and documentation. The authors evaluated the effects of EHR adoption in an oculoplastics practice, which had not been previously studied.

METHODS

Clinical volume, documentation time, time spent with patients, reimbursement, relative value units, and patient satisfaction were examined for 2 academic oculoplastics attendings between April 2018 and April 2019, with EHR implementation in September 2018.

RESULTS

The mean number of patients seen in a half-day clinic was 31.8 versus 27.7 (p = 0.018) pre- and post-EHR implementation, respectively. EHR implementation had no effect on total monthly reimbursement (p = 0.88) or total monthly relative value units (p = 0.54). Average reimbursement (p = 0.004) and relative value units (p = 0.001) per patient encounter were significantly greater with EHR use. Patient satisfaction scores improved (p = 0.018). Mean physician time per patient increased from 6.4 to 9.0 minutes (p < 0.001). Mean documentation time per patient increased from 1.7 to 3.6 minutes (p < 0.001). Average patient wait times decreased by 9 minutes (p = 0.03) with EHR use. No scribes were used.

CONCLUSIONS

EHR implementation was associated with decreased patient volume without significant differences in total reimbursement. Although EHR adoption was associated with increased physician time devoted to patients and greater time expenditure on documentation, patients experienced decreased wait times. This suggests that EHR use streamlined the overall clinic flow without sacrificing physicians' time with the patient. The author's findings suggest that EHR implementation can be accomplished in an academic oculoplastics setting without negative impact on patient experience or reimbursement considerations.