Electronic health record use, intensity of hospital care, and patient outcomes

Racial differences in length of stay and readmission for asthma in the all of us research program

BACKGROUND

This study addresses the limited research on racial disparities in asthma hospitalization outcomes, specifically length of stay (LOS) and readmission, across the U.S.

METHODS

We analyzed in-patient and emergency department visits from the All of Us Research Program, identifying various risk factors (demographic, comorbid, temporal, and place-based) associated with asthma LOS and 30-day readmission using Bayesian mixed-effects models.

RESULTS

Of 17,233 patients (48.0% White, 30.7% Black, 19.7% Hispanic/Latino, 1.3% Asian, and 0.3% Middle Eastern and North African) with 82,188 asthma visits, Black participants had 20% shorter LOS and 12% higher odds of readmission, compared to White participants in multivariate analyses. Public-insured patients had 14% longer LOS and 39% higher readmission odds than commercially insured patients. Weekend admissions resulted in a 12% shorter LOS but 10% higher readmission odds. Asthmatics with chronic diseases had a longer LOS (range: 6-39%) and higher readmission odds (range: 9-32%) except for those with allergic rhinitis, who had a 23% shorter LOS.

CONCLUSIONS

A comprehensive understanding of the factors influencing asthma hospitalization, in conjunction with diverse datasets and clinical-community partnerships, can help physicians and policymakers to systematically address racial disparities, healthcare utilization and equitable outcomes in asthma care.

Association between Electronic Medical Records and Healthcare Quality

The implementation of electronic medical records (EMRs) has generally been thought to improve medical efficiency and safety, but consistent evidence of improved healthcare quality due to EMRs in population-based studies is lacking. We assessed the relationship between the degree of EMR adoption and patient outcomes.We performed an observational study using discharge data from Tri-service General Hospital from 2013 to 2018. The levels of EMR utilization were divided into no EMRs, partial EMRs and full EMRs. The primary healthcare quality indicators were inpatient mortality, readmission within 14 days, and 48-hour postoperative mortality. We performed a Cox proportional hazards regression analysis to evaluate the relationship between the EMR utilization level and healthcare quality.In total, 262,569 patients were included in this study. Compared with no EMRs, full EMR implementation led to lower inpatient mortality [adjusted hazard ratio (HR) 0.947, 95% confidence interval (CI): 0.897-0.999, P = ..049] and a lower risk of readmission within 14 days (adjusted HR 0.627, 95% CI: 0.577-0.681, P < .001). Full EMR implementation was associated was a lower risk of 48-hour postoperative mortality (adjusted HR 0.372, 95% CI: 0.208-0.665, P = .001) than no EMRs. Partial EMR implementation was associated with a higher risk of readmission within 14 days than no EMRs (HR 1.387, 95% CI: 1.298-1.485, P < .001).Full EMR adoption improves healthcare quality in medical institutions treating severely ill patients. A prospective study is needed to confirm this finding.

A Supervised Pattern Analysis of the Length of Stay for Hip Replacement Admissions

Hip replacement is the most common surgical procedure among Medicare patients in the US and worldwide. The hospital length of stay (LOS) for hip replacement admissions is therefore important to be controlled, contributing to savings for hospitals. This study combined medical claims and hospital structure and service data to examine LOS fluctuations and trends, and admission distribution patterns, during weekdays, for hip replacement cases. The study furthermore examined associations of these patterns with the LOS performance. Most hospitals were found to admit hip replacement cases at the start of the week (Monday through Wednesday). There is an upward LOS trend as we approach late weekday admissions. Multiple linear regression analysis showed that LOS weekday inconsistencies, a large proportion of hip replacement admissions on Thursday and Friday, the government ownership status, the bed size, and the critical access status are associated with an increased LOS. On the other hand, the rate of hip replacement admissions over total ones, and the hospital being accredited, are associated with a lower LOS. Findings stress out the need for hospitals to maintain an effective and balanced distribution of hip replacement admissions, evenly during the week, and the need for standardized case management, to avoid practice variability and, therefore, LOS fluctuations for their hip replacement cases.

The use of Electronic Health Records to Support Population Health: A Systematic Review of the Literature

Electronic health records (EHRs) have emerged among health information technology as "meaningful use" to improve the quality and efficiency of healthcare, and health disparities in population health. In other instances, they have also shown lack of interoperability, functionality and many medical errors. With proper implementation and training, are electronic health records a viable source in managing population health? The primary objective of this systematic review is to assess the relationship of electronic health records' use on population health through the identification and analysis of facilitators and barriers to its adoption for this purpose. Authors searched Cumulative Index of Nursing and Allied Health Literature (CINAHL) and MEDLINE (PubMed), 10/02/2012-10/02/2017, core clinical/academic journals, MEDLINE full text, English only, human species and evaluated the articles that were germane to our research objective. Each article was analyzed by multiple reviewers. Group members recognized common facilitators and barriers associated with EHRs effect on population health. A final list of articles was selected by the group after three consensus meetings (n = 55). Among a total of 26 factors identified, 63% (147/232) of those were facilitators and 37% (85/232) barriers. About 70% of the facilitators consisted of productivity/efficiency in EHRs occurring 33 times, increased quality and data management each occurring 19 times, surveillance occurring 17 times, and preventative care occurring 15 times. About 70% of the barriers consisted of missing data occurring 24 times, no standards (interoperability) occurring 13 times, productivity loss occurring 12 times, and technology too complex occurring 10 times. The analysis identified more facilitators than barriers to the use of the EHR to support public health. Wider adoption of the EHR and more comprehensive standards for interoperability will only enhance the ability for the EHR to support this important area of surveillance and disease prevention. This review identifies more facilitators than barriers to using the EHR to support public health, which implies a certain level of usability and acceptance to use the EHR in this manner. The public-health industry should combine their efforts with the interoperability projects to make the EHR both fully adopted and fully interoperable. This will greatly increase the availability, accuracy, and comprehensiveness of data across the country, which will enhance benchmarking and disease surveillance/prevention capabilities.

Prévention du suicide et santé connectée

L’évaluation ponctuelle du risque suicidaire habituellement conduite aux urgences, après un geste suicidaire, ne rend pas compte de son évolution après la sortie des soins, alors même que le risque de récidive reste important plusieurs mois après. Dans ces conditions, les possibilités d’identification, et donc de prise en charge, des patients à risque suicidaire sont limitées. Le développement de la santé connectée (eHealth) donne désormais accès en temps réel à des informations sur l’état de santé d’un patient entre deux séjours en centre de soins. Cette extension de l’évaluation clinique à l’environnement du patient permet de développer des outils d’aide à la décision face à la gestion du risque suicidaire.

Seeing the forest beyond the trees: Predicting survival in burn patients with machine learning

BACKGROUND

This study aims to identify predictors of survival for burn patients at the patient and hospital level using machine learning techniques.

METHODS

The HCUP SID for California, Florida and New York were used to identify patients admitted with a burn diagnosis and merged with hospital data from the AHA Annual Survey. Random forest and stochastic gradient boosting (SGB) were used to identify predictors of survival at the patient and hospital level from the top performing model.

RESULTS

We analyzed 31,350 patients from 670 hospitals. SGB (AUC 0.93) and random forest (AUC 0.82) best identified patient factors such as age and absence of renal failure (p < 0.001) and hospital factors such as full time residents (p < 0.001) and nurses (p = 0.004) to be associated with increased survival.

CONCLUSIONS

Patient and hospital factors are predictive of survival in burn patients. It is difficult to control patient factors, but hospital factors can inform decisions about where burn patients should be treated.

From eHealth to iHealth: Transition to Participatory and Personalized Medicine in Mental Health

Clinical assessment in psychiatry is commonly based on findings from brief, regularly scheduled in-person appointments. Although critically important, this approach reduces assessment to cross-sectional observations that miss essential information about disease course. The mental health provider makes all medical decisions based on this limited information. Thanks to recent technological advances such as mobile phones and other personal devices, electronic health (eHealth) data collection strategies now can provide access to real-time patient self-report data during the interval between visits. Since mobile phones are generally kept on at all times and carried everywhere, they are an ideal platform for the broad implementation of ecological momentary assessment technology. Integration of these tools into medical practice has heralded the eHealth era. Intelligent health (iHealth) further builds on and expands eHealth by adding novel built-in data analysis approaches based on (1) incorporation of new technologies into clinical practice to enhance real-time self-monitoring, (2) extension of assessment to the patient's environment including caregivers, and (3) data processing using data mining to support medical decision making and personalized medicine. This will shift mental health care from a reactive to a proactive and personalized discipline.

Impact of an Intervention to Improve Weekend Hospital Care at an Academic Medical Center: An Observational Study

BACKGROUND

Hospital care on weekends has been associated with delays in care, reduced quality, and poor clinical outcomes.

OBJECTIVE

The purpose of this study was to evaluate the impact of a weekend hospital intervention on processes of care and clinical outcomes. The multifaceted intervention included expanded weekend diagnostic services, improved weekend discharge processes, and increased physician and care management services on weekends.

DESIGN AND PATIENTS

This was an interrupted time series observational study of adult non-obstetric patients hospitalized at a single academic medical center between January 2011 and January 2014. The study included 18 months prior to and 19 months following the implementation of the intervention. Data were analyzed using segmented regression analysis with adjustment for confounders.

MAIN MEASURES

The primary outcome was average length of stay. Secondary outcomes included percent of patients discharged on weekends, 30-day readmission rate, and in-hospital mortality rate.

KEY RESULTS

The study included 57,163 hospitalizations. Following implementation of the intervention, average length of stay decreased by 13 % (95 % CI 10-15 %) and continued to decrease by 1 % (95 % CI 1-2 %) per month as compared to the underlying time trend. The proportion of weekend discharges increased by 12 % (95 % CI 2-22 %) at the time of the intervention and continued to increase by 2 % (95 % CI 1-3 %) per month thereafter. The intervention had no impact on readmissions or mortality. During the post-implementation period, the hospital was evacuated and closed for 2 months due to damage from Hurricane Sandy, and a new hospital-wide electronic health record was introduced. The contributions of these events to our findings are not known. We observed a lower inpatient census and found differences in patient characteristics, including higher rates of Medicaid insurance and comorbidities, in the post-Hurricane Sandy period as compared to the pre-Sandy period.

CONCLUSIONS

The intervention was associated with a reduction in length of stay and an increase in weekend discharges. Our longitudinal study also illuminated the challenges of evaluating the effectiveness of a large-scale intervention in a real-world hospital setting.

Components of Hospital Perioperative Infrastructure Can Overcome the Weekend Effect in Urgent General Surgery Procedures

OBJECTIVE

We hypothesized that perioperative hospital resources could overcome the "weekend effect" (WE) in patients undergoing emergent/urgent surgeries.

SUMMARY BACKGROUND DATA

The WE is the observation that surgeon-independent patient outcomes are worse on the weekend compared with weekdays. The WE is often explained by differences in staffing and resources resulting in variation in care between the week and weekend.

METHODS

Emergent/urgent surgeries were identified using the Healthcare Cost and Utilization Project State Inpatient Database (Florida) from 2007 to 2011 and linked to the American Hospital Association (AHA) Annual Survey Database to determine hospital level characteristics. Extended median length of stay (LOS) on the weekend compared with the weekdays (after controlling for hospital, year, and procedure type) was selected as a surrogate for WE.

RESULTS

Included were 126,666 patients at 166 hospitals. A total of 17 hospitals overcame the WE during the study period. Logistic regression, controlling for patient characteristics, identified full adoption of electronic medical records (OR 4.74), home health program (OR 2.37), pain management program [odds ratio (OR) 1.48)], increased registered nurse-to-bed ratio (OR 1.44), and inpatient physical rehabilitation (OR 1.03) as resources that were predictors for overcoming the WE. The prevalence of these factors in hospitals exhibiting the WE for all 5 years of the study period were compared with those hospitals that overcame the WE (P < 0.001).

CONCLUSIONS

Specific hospital resources can overcome the WE seen in urgent general surgery procedures. Improved hospital perioperative infrastructure represents an important target for overcoming disparities in surgical care.

Association of weekend continuity of care with hospital length of stay

OBJECTIVE

The purpose of this study was to evaluate the association of physician continuity of care with length of stay, likelihood of weekend discharge, in-hospital mortality and 30-day readmission.

DESIGN

A cohort study of hospitalized medical patients. The primary exposure was the weekend usual provider continuity (UPC) over the initial weekend of care. This metric was adapted from an outpatient continuity of care index. Regression models were developed to determine the association between UPC and outcomes.

SETTING

An academic medical center.

MAIN OUTCOME MEASURE

Length of stay which was calculated as the number of days from the first Saturday of the hospitalization to the day of discharge.

RESULTS

Of the 3391 patients included in this study, the prevalence of low, moderate and high UPC for the initial weekend of hospitalization was 58.7, 22.3 and 19.1%, respectively. When compared with low continuity of care, both moderate and high continuity of care were associated with reduced length of stay, with adjusted rate ratios of 0.92 (95% CI 0.86-1.00) and 0.64 (95% CI 0.53-0.76), respectively. High continuity of care was associated with likelihood of weekend discharge (adjusted odds ratio 2.84, 95% CI 2.11-3.83) but was not significantly associated with mortality (adjusted odds ratio 0.72, 95% CI 0.29-1.80) or readmission (adjusted odds ratio 0.88, 95% CI 0.68-1.14) when compared with low continuity of care.

CONCLUSIONS

Increased weekend continuity of care is associated with reduced length of stay. Improvement in weekend cross-coverage and patient handoffs may be useful to improve clinical outcomes.