Mental Health Service Use Before and After a Suicidal Crisis Among Children and Adolescents in a United States National Medicaid Sample.

INTRODUCTION: Mental health follow-up after an emergency department (ED) visit for suicide ideation/attempt is a critical component of suicide prevention for young people. METHODS: We analyzed 2009 to 2012 Medicaid Analytic EXtract for 62,139 treat-and-release ED visits and 30,312 ED-to-hospital admissions for suicide ideation/attempt among patients ages 6 to 17 years. We used mixed-effects logistic regression models to examine associations between patients' health care utilization prior to the ED visit and likelihood of completing a 30-day mental health follow-up visit. RESULTS: Overall, for treat-and-release ED visits, 49% had a 30-day follow-up mental health visit, and for ED-to-hospital admissions, 67% had a 30-day follow-up mental health visit. Having a mental health visit in the 30 days preceding the ED visit was the strongest predictor of completing a mental health follow-up visit (ED treat-and-release: adjusted odds ratio [AOR] 11.01; 95% confidence interval [CI] 9.82-12.35; ED-to-hospital AOR 4.60; 95% CI 3.16-6.68). Among those with no mental health visit in the 30 days preceding the ED visit, only 25% had an ambulatory mental health follow-up visit. Having a general health care visit in the 30 days preceding the ED visit had a much smaller association with completing a mental health follow-up visit (ED treat-and-release: AOR 1.17; 95% CI 1.09-1.24; ED-to-hospital AOR 1.25; 95% CI 1.17-1.34). CONCLUSIONS: Young people without an existing source of ambulatory mental health care have low rates of mental health follow-up after an ED visit for suicide ideation or attempt, and opportunities exist to improve mental health follow-up for youth with recent general health care visits.

The Impact of Delayed Symptomatic Treatment Implementation in the Intensive Care Unit.

We estimated the harm related to medication delivery delays across 12,474 medication administration instances in an intensive care unit using retrospective data in a large urban academic medical center between 2012 and 2015. We leveraged an instrumental variables (IV) approach that addresses unobserved confounds in this setting. We focused on nurse shift changes as disruptors of timely medication (vasodilators, antipyretics, and bronchodilators) delivery to estimate the impact of delay. The average delay around a nurse shift change was 60.8 min (p < 0.001) for antipyretics, 39.5 min (p < 0.001) for bronchodilators, and 57.1 min (p < 0.001) for vasodilators. This delay can increase the odds of developing a fever by 32.94%, tachypnea by 79.5%, and hypertension by 134%, respectively. Compared to estimates generated by a naïve regression approach, our IV estimates tend to be higher, suggesting the existence of a bias from providers prioritizing more critical patients.

The Impact of E-Visits on Visit Frequencies and Patient Health: Evidence from Primary Care.

Secure messaging, or "e-visits," between patients and providers has sharply increased in recent years, and many hope they will help improve healthcare quality, while increasing provider capacity. Using a panel data set from a large healthcare system in the United States, we find that e-visits trigger about 6% more office visits, with mixed results on phone visits and patient health. These additional visits come at the sacrifice of new patients: physicians accept 15% fewer new patients each month following e-visit adoption. Our data set on nearly 100,000 patients spans from 2008 to 2013, which includes the rollout and diffusion of e-visits in the health system we study. Identification comes from difference-in-differences estimates leveraging variation in the timing of e-visit adoption by both patients and providers. We conduct several robustness checks, including matching analyses and an instrumental variable analysis to account for possible time-varying characteristics among patient e-visit adopters.

Effect of Electronic Reminders, Financial Incentives, and Social Support on Outcomes After Myocardial Infarction: The HeartStrong Randomized Clinical Trial.

Importance: Adherence to medications prescribed after acute myocardial infarction (AMI) is low. Wireless technology and behavioral economic approaches have shown promise in improving health behaviors. Objective: To determine whether a system of medication reminders using financial incentives and social support delays subsequent vascular events in patients following AMI compared with usual care. Design, Setting, and Participants: Two-arm, randomized clinical trial with a 12-month intervention conducted from 2013 through 2016. Investigators were blinded to study group, but participants were not. Design was a health plan-intermediated intervention for members of several health plans. We recruited 1509 participants from 7179 contacted AMI survivors (insured with 5 large US insurers nationally or with Medicare fee-for-service at the University of Pennsylvania Health System). Patients aged 18 to 80 years were eligible if currently prescribed at least 2 of 4 study medications (statin, aspirin, ß-blocker, antiplatelet agent), and were hospital inpatients for 1 to 180 days and discharged home with a principal diagnosis of AMI. Interventions: Patients were randomized 2:1 to an intervention using electronic pill bottles combined with lottery incentives and social support for medication adherence (1003 patients), or to usual care (506 patients). Main Outcomes and Measures: Primary outcome was time to first vascular rehospitalization or death. Secondary outcomes were time to first all-cause rehospitalization, total number of repeated hospitalizations, medication adherence, and total medical costs. Results: A total of 35.5% of participants were female (n = 536); mean (SD) age was 61.0 (10.3) years. There were no statistically significant differences between study arms in time to first rehospitalization for a vascular event or death (hazard ratio, 1.04; 95% CI, 0.71 to 1.52; P = .84), time to first all-cause rehospitalization (hazard ratio, 0.89; 95% CI, 0.73 to 1.09; P = .27), or total number of repeated hospitalizations (hazard ratio, 0.94; 95% CI, 0.60 to 1.48; P = .79). Mean (SD) medication adherence did not differ between control (0.42 [0.39]) and intervention (0.46 [0.39]) (difference, 0.04; 95% CI, -0.01 to 0.09; P = .10). Mean (SD) medical costs in 12 months following enrollment did not differ between control ($29 811 [$74 850]) and intervention ($24 038 [$66 915]) (difference, -$5773; 95% CI, -$13 682 to $2137; P = .15). Conclusions and Relevance: A compound intervention integrating wireless pill bottles, lottery-based incentives, and social support did not significantly improve medication adherence or vascular readmission outcomes for AMI survivors. Trial Registration: clinicaltrials.gov Identifier: NCT01800201.

Characterizing Primary Care Visit Activities at Veterans Health Administration Clinics.

Medical home models seek to increase efficiency and maximize the use of resources by ensuring that all care team members work at the top of their licenses. We sought to break down primary care office visits into measurable activities to better under stand how primary care providers (PCPs) currently spend visit time and to provide insight into potential opportunities for revision or redistribution of healthcare tasks. We videotaped 27 PCPs during office visits with 121 patients at four Veterans Health Administration medical centers. Based on patterns emerging from the data, we identified a taxonomy of 12 provider activity categories that enabled us to quantify the frequency and duration of activities occurring during routine primary care visits. We conducted descriptive and multivariate analyses to examine associations between visit characteristics and provider and clinic characteristics. We found that PCPs spent the greatest percentage of their visit time discussing existing conditions (20%), discussing new conditions (18%), record keeping (13%), and examining patients (13%). Providers spent the smallest percentage of time on preventive care and coordination of care. Mean visit length was 22.9 minutes (range 7.9-58.0 minutes). Site-level ratings of medical home implementation were not associated with differences in how visit time was spent. These data provide a window into how PCPs are spending face-to-face time with patients. The methodology and taxonomy presented here may prove useful for future quality improvement and research endeavors, particularly those focused on opportunities to increase nonappointment care and to ensure that team members work at the top of their skill level.

Rethinking primary care visits: how much can be eliminated, delegated or performed outside of the face-to-face visit?

RATIONALE, AIMS AND OBJECTIVES: Office visits represent the core component of primary care practice, but little is known about what percentage of primary care provider (PCP) visit time could be suitable for reassignment to another medical home team member or to a non-face-to-face modality (e.g. secure messaging) in order to optimize face-to-face PCP visit time. METHOD: We videotaped 121 PCP office visits at four Veterans Health Administration Medical Centers and divided visits into discrete activity segments. Two physicians reviewed each visit recording and provided independent clinical judgments regarding which segments might be suitable for reassignment. We examined the activity category distribution of visit time rated as needing face-to-face time with a PCP. RESULTS: Reviewers judged 53% of the 5398 minutes of rated visit time as suitable for reassignment to another team member or modality. The percentage of time rated as needing face-to-face PCP care varied greatly by activity category, from a high of 73.9% (for examining patients) to a low of 16.2% (for medication review). Rater agreement regarding tasks' suitability for reassignment varied across activity categories. CONCLUSIONS: These data offer an example of how face-to-face PCP visit time might be optimized as practices seek to shift components of patient care to other team members and other modalities. Given variations in provider preferences and judgments, successful redesign efforts will need to involve stakeholders in decisions about how to best utilize medical home resources.

Primary care access and emergency room use among older veterans.

BACKGROUND: Patient access to primary care is often noted to be poor. Improving access may reduce emergency room (ER) visits. OBJECTIVE: To examine the relationship between primary care access and ER use and to test whether this relationship is moderated by having a continuous relationship with a Primary Care Provider (PCP) (or if the PCP is the near-sole provider of care for patients). DESIGN AND PATIENTS: A longitudinal retrospective study of 627,276 patients receiving primary care from 6,398 primary care providers (PCPs) nationally within the Veterans Health Administration (VHA) in 2009. We tracked weekly changes in PCP-level appointment availability. MEASUREMENTS: The number of a PCP's patients who went to the ER in a given week. RESULTS: Among all PCPs, being absent from patient care for the week had no effect on whether that PCP's patients used the ER in that week (incident rate ratio (IRR) 0.997, p = 0.70). However, among PCPs who were near-sole providers of care, a PCP's absence for a week or more had a statistically significant effect on ER visits (IRR 1.04, p = 0.01). The percentage of a PCP's weekly appointment slots that were fully booked (booking density) had no significant effect on whether their patients used the ER in that week among all PCPs. However, among near-sole providers of care, a 10-percentage point increase in the booking density changed the IRR of ER visits in that week by 1.005 (p = 0.08) and by 1.006 on weekdays (p = 0.07). CONCLUSIONS: Patients' access to their PCP had a small effect on whether those patients used the ER among PCPs whose patients rarely saw another PCP. Among other PCPs, there was no effect of PCP access on ER use. These results suggest that sharing patient-care responsibilities across PCPs may be effective in improving access to care and decreasing unnecessary ER use.

Innovating in health delivery: The Penn medicine innovation tournament.

BACKGROUND: Innovation tournaments can drive engagement and value generation by shifting problem-solving towards the end user. In health care, where the frontline workers have the most intimate understanding of patients' experience and the delivery process, encouraging them to generate and develop new approaches is critical to improving health care delivery. PROBLEM: In many health care organizations, senior managers and clinicians retain control of innovation. Frontline workers need to be engaged in the innovation process. GOALS: Penn Medicine launched a system-wide innovation tournament with the goal of improving the patient experience. We set a quantitative goal of receiving 500 ideas and getting at least 1000 employees to participate in the tournament. A secondary goal was to involve various groups of the care process (doctors, nurses, clerical staff, transporters). STRATEGY: The tournament was broken up into three phases. During Phase 1, employees were encouraged to submit ideas. Submissions were judged by an expert panel and crowd sourcing based on their potential to improve patient experience and ability to be implemented within 6 months. During Phase 2, the best 200 ideas were pitched during a series of 5 workshops and ten finalists were selected. During Phase 3, the best 10 ideas were presented to and judged by an audience of about 200 interested employees and a judging panel of 15 administrators. Two winners were selected. RESULTS: A total of 1739 ideas were submitted and over 5000 employees participated in the innovation tournament. Patient convenience/amenities (21%) was the top category of submission, with other popular areas including technology optimization (11%), assistance with navigation within UPHS (10%), and improving patient/family centered care (9%) and care delivery models/transitions (9%). A combination of winning and submitted ideas were implemented.

Provider variation in fast track treatment time.

OBJECTIVE: To examine the degree to which fast track (FT) treatment time varies among providers. METHODS: A retrospective cohort study that included 105,783 FT visits at 3 emergency departments (EDs) during a 3-year period. We calculated the median treatment time for 80 primary providers (physicians and physician extenders) and 109 nurses (2 sites only). We used a hierarchical linear regression model that accounted for the clustering of patient visits to the same provider to estimate each provider's median treatment time controlling for patient, clinical, temporal, and ED demand (ie, number of arrivals) characteristics. RESULTS: Median FT treatment time across the 3 sites ranged from 48 to 134 minutes. Adjusted for other factors, the median FT treatment time of providers at the 90th versus 10th percentiles was 1.4 to 2.6 times longer across the 3 sites. The variation by FT nurses was also large. The median FT treatment time of nurses at the 90th versus 10th percentiles was 1.5 and 1.4 times longer at sites A and C, respectively. At all sites, provider and clinical factors explained more variation in FT treatment time than patient, ED demand, or temporal factors. CONCLUSIONS: There were clinically meaningful differences in FT treatment time among the providers at all sites. Given that the providers share the same environment and patient population, understanding why such large provider variation in FT treatment time exists warrants further investigation.

Direct observation of bed utilization in the pediatric intensive care unit.

BACKGROUND: The pediatric intensive care unit (PICU), with limited number of beds and resource-intensive services, is a key component of patient flow. Because the PICU is a crossroads for many patients, transfer or discharge delays can negatively impact a patient's clinical status and efficiency. OBJECTIVE: The objective of this study was to describe, using direct observation, PICU bed utilization. METHODS: We conducted a real-time, prospective observational study in a convenience sample of days in the PICU of an urban, tertiary-care children's hospital. RESULTS: Among 824 observed hours, 19,887 bed-hours were recorded, with 82% being for critical care services and 18% for non-critical care services. Fourteen activities accounted for 95% of bed-hours. Among 200 hours when the PICU was at full capacity, 75% of the time included at least 1 bed that was used for non-critical care services; 37% of the time at least 2 beds. The mean waiting time for a floor bed assignment was 9 hours (median, 5.5 hours) and accounted for 4.62% of all bed-hours observed. CONCLUSIONS: The PICU delivered critical care services most of the time, but periods of non-critical care services represented a significant amount of time. In particular, periods with no bed available for new patients were associated with at least 1 or more PICU beds being used for non-critical care activities. The method should be reproducible in other settings to learn more about the structure and processes of care and patient flow and to make improvements.

Emergency medicine: an operations management view.

Operations management (OM) is the science of understanding and improving business processes. For the emergency department (ED), OM principles can be used to reduce and alleviate the effects of crowding. A fundamental principle of OM is the waiting time formula, which has clear implications in the ED given that waiting time is fundamental to patient-centered emergency care. The waiting time formula consists of the activity time (how long it takes to complete a process), the utilization rate (the proportion of time a particular resource such a staff is working), and two measures of variation: the variation in patient interarrival times and the variation in patient processing times. Understanding the waiting time formula is important because it presents the fundamental parameters that can be managed to reduce waiting times and length of stay. An additional useful OM principle that is applicable to the ED is the efficient frontier. The efficient frontier compares the performance of EDs with respect to two dimensions: responsiveness (i.e., 1/wait time) and utilization rates. Some EDs may be "on the frontier," maximizing their responsiveness at their given utilization rates. However, most EDs likely have opportunities to move toward the frontier. Increasing capacity is a movement along the frontier and to truly move toward the frontier (i.e., improving responsiveness at a fixed capacity), we articulate three possible options: eliminating waste, reducing variability, or increasing flexibility. When conceptualizing ED crowding interventions, these are the major strategies to consider.

Working with capacity limitations: operations management in critical care.

As your hospital's ICU director, you are approached by the hospital's administration to help solve ongoing problems with ICU bed availability. The ICU seems to be constantly full, and trauma patients in the emergency department sometimes wait up to 24 hours before receiving a bed. Additionally, the cardiac surgeons were forced to cancel several elective coronary-artery bypass graft cases because there was not a bed available for postoperative recovery. The hospital administrators ask whether you can decrease your ICU length of stay, and wonder whether they should expand the ICU to include more beds For help in understanding and optimizing your ICU's throughput, you seek out the operations management researchers at your university.

The financial consequences of lost demand and reducing boarding in hospital emergency departments.

STUDY OBJECTIVE: Some have suggested that emergency department (ED) boarding is prevalent because it maximizes revenue as hospitals prioritize non-ED admissions, which reimburse higher than ED admissions. We explore the revenue implications to the overall hospital of reducing boarding in the ED. METHODS: We quantified the revenue effect of reducing boarding-the balance of higher ED demand and the reduction of non-ED admissions-using financial modeling informed by regression analysis and discrete-event simulation with data from 1 inner-city teaching hospital during 2 years (118,000 ED visits, 22% ED admission rate, 7% left without being seen rate, 36,000 non-ED admissions). Various inpatient bed management policies for reducing non-ED admissions were tested. RESULTS: Non-ED admissions generated more revenue than ED admissions ($4,118 versus $2,268 per inpatient day). A 1-hour reduction in ED boarding time would result in $9,693 to $13,298 of additional daily revenue from capturing left without being seen and diverted ambulance patients. To accommodate this demand, we found that simulated management policies in which non-ED admissions are reduced without consideration to hospital capacity (ie, static policies) mostly did not result in higher revenue. Many dynamic policies requiring cancellation of various proportions of non-ED admissions when the hospital reaches specific trigger points increased revenue. The optimal strategies tested resulted in an estimated $2.7 million and $3.6 in net revenue per year, depending on whether left without being seen patients were assumed to be outpatients or mirrored ambulatory admission rates, respectively. CONCLUSION: Dynamic inpatient bed management in inner-city teaching hospitals in which non-ED admissions are occasionally reduced to ensure that EDs have reduced boarding times is a financially attractive strategy.