Effectiveness of a Self-Administered Computerized Mental Health Screening Tool in the Emergency Department.

OBJECTIVE: The authors sought to determine the effectiveness of a self-administered computerized mental health screening tool in a general acute care emergency department (ED). METHODS: Changes in patient care (diagnosis of a past-year psychiatric disorder, request for psychiatric consultation, psychiatric referral at discharge, or transfer to psychiatric facility) and patient ED return visits (3 months after discharge vs. 3 months before) were assessed among ED physicians (N=451) who received patients' computerized screening reports (N=207) and those who did not (N=244). All patients received copies of screening results. RESULTS: The computerized mental health screening tool identified previously undiagnosed psychiatric problems. However, no statistically significant differences were found in physician care or patient ED return visits. CONCLUSIONS: Computerized mental health screening did not result in further psychiatric diagnoses or treatment; it also did not significantly reduce patient ED return visits. Collaboration among EDs and mental health treatment agencies, organizations, and researchers is needed to facilitate appropriate treatment referrals and linkage.

Modeling Patient No-Show History and Predicting Future Outpatient Appointment Behavior in the Veterans Health Administration.

BACKGROUND: Missed appointments reduce the efficiency of the health care system and negatively impact access to care for all patients. Identifying patients at risk for missing an appointment could help health care systems and providers better target interventions to reduce patient no-shows. OBJECTIVES: Our aim was to develop and test a predictive model that identifies patients that have a high probability of missing their outpatient appointments. METHODS: Demographic information, appointment characteristics, and attendance history were drawn from the existing data sets from four Veterans Affairs health care facilities within six separate service areas. Past attendance behavior was modeled using an empirical Markov model based on up to 10 previous appointments. Using logistic regression, we developed 24 unique predictive models. We implemented the models and tested an intervention strategy using live reminder calls placed 24, 48, and 72 hours ahead of time. The pilot study targeted 1,754 high-risk patients, whose probability of missing an appointment was predicted to be at least 0.2. RESULTS: Our results indicate that three variables were consistently related to a patient's no-show probability in all 24 models: past attendance behavior, the age of the appointment, and having multiple appointments scheduled on that day. After the intervention was implemented, the no-show rate in the pilot group was reduced from the expected value of 35% to 12.16% (p value < 0.0001). CONCLUSIONS: The predictive model accurately identified patients who were more likely to miss their appointments. Applying the model in practice enables clinics to apply more intensive intervention measures to high-risk patients.

Implementation of Lung Cancer Screening in the Veterans Health Administration.

Importance: The US Preventive Services Task Force recommends annual lung cancer screening (LCS) with low-dose computed tomography for current and former heavy smokers aged 55 to 80 years. There is little published experience regarding implementing this recommendation in clinical practice. Objectives: To describe organizational- and patient-level experiences with implementing an LCS program in selected Veterans Health Administration (VHA) hospitals and to estimate the number of VHA patients who may be candidates for LCS. Design, Setting, and Participants: This clinical demonstration project was conducted at 8 academic VHA hospitals among 93 033 primary care patients who were assessed on screening criteria; 2106 patients underwent LCS between July 1, 2013, and June 30, 2015. Interventions: Implementation Guide and support, full-time LCS coordinators, electronic tools, tracking database, patient education materials, and radiologic and nodule follow-up guidelines. Main Outcomes and Measures: Description of implementation processes; percentages of patients who agreed to undergo LCS, had positive findings on results of low-dose computed tomographic scans (nodules to be tracked or suspicious findings), were found to have lung cancer, or had incidental findings; and estimated number of VHA patients who met the criteria for LCS. Results: Of the 4246 patients who met the criteria for LCS, 2452 (57.7%) agreed to undergo screening and 2106 (2028 men and 78 women; mean [SD] age, 64.9 [5.1] years) underwent LCS. Wide variation in processes and patient experiences occurred among the 8 sites. Of the 2106 patients screened, 1257 (59.7%) had nodules; 1184 of these patients (56.2%) required tracking, 42 (2.0%) required further evaluation but the findings were not cancer, and 31 (1.5%) had lung cancer. A variety of incidental findings, such as emphysema, other pulmonary abnormalities, and coronary artery calcification, were noted on the scans of 857 patients (40.7%). Conclusions and Relevance: It is estimated that nearly 900 000 of a population of 6.7 million VHA patients met the criteria for LCS. Implementation of LCS in the VHA will likely lead to large numbers of patients eligible for LCS and will require substantial clinical effort for both patients and staff.

Time required to review research protocols at 10 Veterans Affairs Institutional Review Boards.

BACKGROUND: Despite perceptions that institutional review boards (IRBs) delay research, little is known about how long it takes to secure IRB approval. We retrospectively quantified IRB review times at 10 large Veterans Affairs (VA) IRBs. METHODS: We collected IRB records pertaining to a stratified random sample of research protocols drawn from 10 of the 26 largest VA IRBs. Two independent analysts abstracted dates from the IRB records, from which we calculated overall and incremental review times. We used multivariable linear regression to assess variation in total and incremental review times by IRB and review level (i.e., exempt, expedited, or full board) and to identify potential targets for efforts to improve the efficiency and uniformity of the IRB review process. RESULTS: In a sample of 277 protocols, the mean review time was 112 d (95% confidence interval [CI]: 105-120). Compared with full-board reviews at IRB 1, average review times at IRBs 3, 8, 9, and 10 were 27 (95% CI: 6-48), 37 (95% CI: 11-63), 45 (95% CI: 20-69), and 24 (95% CI: 2-45) d shorter, and at IRB 6, times were 56 (95% CI: 28-84) d longer. Across all IRBs, expedited reviews were 44 (95% CI: 30-58) d shorter on average than were full-board reviews, with no significant difference between exempt and full-board reviews. However, after subtracting the time required for Research and Development Committee review, exempt reviews were 21 (95% CI: 1-41) d shorter on average than were full-board reviews. CONCLUSIONS: IRB review times differ significantly by IRB and review level. Few VA IRBs approach a consensus panel goal of 60 d for IRB review. The unexpectedly longer review times for exempt protocols in the VA can be attributed to time required for Research and Development Committee review. Prospective, routine collection of key time points in the IRB review process could inform IRB-specific initiatives for reducing VA IRB review times.

Large-Scale No-Show Patterns and Distributions for Clinic Operational Research.

Patient no-shows for scheduled primary care appointments are common. Unused appointment slots reduce patient quality of care, access to services and provider productivity while increasing loss to follow-up and medical costs. This paper describes patterns of no-show variation by patient age, gender, appointment age, and type of appointment request for six individual service lines in the United States Veterans Health Administration (VHA). This retrospective observational descriptive project examined 25,050,479 VHA appointments contained in individual-level records for eight years (FY07-FY14) for 555,183 patients. Multifactor analysis of variance (ANOVA) was performed, with no-show rate as the dependent variable, and gender, age group, appointment age, new patient status, and service line as factors. The analyses revealed that males had higher no-show rates than females to age 65, at which point males and females exhibited similar rates. The average no-show rates decreased with age until 75-79, whereupon rates increased. As appointment age increased, males and new patients had increasing no-show rates. Younger patients are especially prone to no-show as appointment age increases. These findings provide novel information to healthcare practitioners and management scientists to more accurately characterize no-show and attendance rates and the impact of certain patient factors. Future general population data could determine whether findings from VHA data generalize to others.

Implementation of new clinical programs in the VHA healthcare system: the importance of early collaboration between clinical leadership and research.

Collaboration between policy, research, and clinical partners is crucial to achieving proven quality care. The Veterans Health Administration has expended great efforts towards fostering such collaborations. Through this, we have learned that an ideal collaboration involves partnership from the very beginning of a new clinical program, so that the program is designed in a way that ensures quality, validity, and puts into place the infrastructure necessary for a reliable evaluation. This paper will give an example of one such project, the Lung Cancer Screening Demonstration Project (LCSDP). We will outline the ways that clinical, policy, and research partners collaborated in design, planning, and implementation in order to create a sustainable model that could be rigorously evaluated for efficacy and fidelity. We will describe the use of the Donabedian quality matrix to determine the necessary characteristics of a quality program and the importance of the linkage with engineering, information technology, and clinical paradigms to connect the development of an on-the-ground clinical program with the evaluation goal of a learning healthcare organization. While the LCSDP is the example given here, these partnerships and suggestions are salient to any healthcare organization seeking to implement new scientifically proven care in a useful and reliable way.

Perioperative process errors and delays in otolaryngology at a Veterans Hospital: prospective study.

OBJECTIVES/HYPOTHESIS: To understand the leading causes for process errors and delays in the otolaryngology operating room and recognize the impact of process errors and delays on patient safety, operating room resources and hospital costs. STUDY DESIGN: Prospective, observational study. METHODS: A 4-week study was conducted during 1 calendar month in 2012, evaluating 23 elective otolaryngology cases. A standardized data collection tool was developed and refined based on prestudy pilot observations. Two trained observers recorded relevant times and actions from patient check-in time in the preoperative holding area to the "wheels out" time. RESULTS: The mean case observation time was 220.0 ± 167.8 minutes, with mean duration of operation length being 107.0 ± 146.2 minutes. The perioperative period was divided into six stages: patient holding, room preparation, preintubation, postintubation, intraoperative, and postextubation. One hundred process errors were recorded (average of 4.3 per case), 34% of which were due to communication failures. Forty delays were observed, resulting in 336 minutes of standstill delay. Again, communication failures represented the most common etiology, with 17 communication failures resulting in 146 minutes of standstill delay. The preintubation stage was most affected by delay, with 1 in 6 minutes comprising standstill delay. CONCLUSION: Process errors and significant delays were common in cases performed at our institution; communication errors were the most common etiology. There is opportunity for preoperative team discussion and the use of technology to minimize communication-related process errors and standstill delays. Further work is currently being undertaken to study this critical issue across specialties.