Hospital Characteristics are Associated With Readiness to Attain Stage 2 Meaningful Use of Electronic Health Records

Predicting and responding to change: Perceived environmental uncertainty among substance use disorder treatment programs

INTRODUCTION

Substance use disorder (SUD) treatment programs offering addiction health services (AHS) must be prepared to adapt to change in their operating environment. These environmental uncertainties may have implications for service delivery, and ultimately patient outcomes. To adapt to a multitude of environmental uncertainties, treatment programs must be prepared to predict and respond to change. Yet, research on treatment programs preparedness for change is sparse. We examined reported difficulties in predicting and responding to changes in the AHS system, and factors associated with these outcomes.

METHODS

Cross-sectional surveys of SUD treatment programs in the United States in 2014 and 2017. We used linear and ordered logistic regression to examine associations between key independent variables (e.g., program, staff, and client characteristics) and four outcomes, (1) reported difficulties in predicting change, (2) predicting effect of change on organization, (3) responding to change, and (4) predicting changes to make to respond to environmental uncertainties. Data were collected through telephone surveys.

RESULTS

The proportion of SUD treatment programs reporting difficulty predicting and responding to changes in the AHS system decreased from 2014 to 2017. However, a considerable proportion still reported difficulty in 2017. We identified that different organizational characteristics are associated with their reported ability to predict or respond to environmental uncertainty. Findings show that predicting change is significantly associated with program characteristics only, while predicting effect of change on organizations is associated with program and staff characteristics. Deciding how to respond to change is associated with program, staff, and client characteristics, while predicting changes to make to respond is associated with staff characteristics only.

CONCLUSIONS

Although treatment programs reported decreased difficulty predicting and responding to changes, our findings identify program characteristics and attributes that could better position programs with the foresight to more effectively predict and respond to uncertainties. Given resource constraints at multiple levels in treatment programs, this knowledge might help identify and optimize aspects of programs to intervene upon to enhance their adaptability to change. These efforts may positively influences processes or care delivery, and ultimately translate into improvements in patient outcomes.

The role of organizational culture in health information technology implementations: A scoping review

Introduction

The exponential growth in health information technology (HIT) presents an immense opportunity for facilitating the data-to-knowledge-to-performance loop which supports learning health systems. This scoping review addresses the gap in knowledge around HIT implementation contextual factors such as organizational culture and provides a current state assessment.

Methods

A search of 13 databases guided by Arskey and O'Malley's framework identified content on HIT implementations and organizational culture. The Consolidated Framework for Implementation Research (CFIR) was used to assess culture and to develop review criteria. Culture stress, culture effort, implementation climate, learning climate, readiness for implementation, leadership engagement, and available resources were the constructs examined. Rayyan and Qualtrics were used for screening and data extraction.

Results

Fifty two studies included were mainly conducted in Academic Health Centers (n = 18, 35%) and at urban locations (n = 50, 96%). Interviews frequently used for data collection (n = 26, 50%) and guided by multiple frameworks (n = 34). Studies mostly focused on EHR implementations (n = 23, 44%) followed by clinical decision support (n = 9, 17%). About two-thirds (n = 34, 65%) reflected culture stress theme and 62% (21 of 34) acknowledged it as a barrier. Culture effort identified in 27 studies and was a facilitator in most (78%, 21 of 27). Leadership engagement theme in majority studies (71%, n = 37), with 35% (n = 13) noting it as a facilitator. Eighty percent (42 studies) noted available resources, 12 of which identified this as barrier to successful implementation.

Conclusions

It is vital to determine the culture and other CFIR inner setting constructs that are significant to HIT implementation as facilitators or barriers. This scoping review presents a limited number of empirical studies in this topic highlighting the need for additional research to quantify the effects of culture. This will help build evidence and best practices that facilitate HIT implementations and hence serve as a platform to support robust learning health systems.

Healthcare providers' readiness for electronic health record adoption: a cross-sectional study during pre-implementation phase

BACKGROUND

The adoption of an electronic health record (EHR) in the healthcare system has the potential to make healthcare service delivery effective and efficient by providing accurate, up-to-date, and complete information. Despite its great importance, the adoptions of EHR in low-income country settings, like Ethiopia, were lagging and increasingly failed. Assessing the readiness of stakeholders before the actual adoption of EHR is considered the prominent solution to tackle the problem. However, little is known about healthcare providers' EHR readiness in this study setting. Accordingly, this research was conducted aiming at examining healthcare providers' readiness for EHR adoption and associated factors in southwestern Ethiopia.

METHODS

An institutional-based cross-sectional study was conducted from September 1 to October 30, 2021. A total of 423 healthcare providers working in public hospitals were selected using a simple random sampling technique. Multivariable logistic regression was fitted to identify determinant factors of overall healthcare providers' readiness after the other covariates were controlled.

RESULT

In this study, the overall good readiness level of EHR adoption was 52.8% (n = 204) [95% CI of 47.9% to 56.6%]. Age, computer literacy, computer access at health facilities, attitude towards EHRs, awareness about EHRs, perceived benefit, and perceived technology self-efficacy were significantly associated with the overall health care providers' readiness for the adoption of EHR using a cut point of P-value less than 0.05.

CONCLUSION

Around half of the respondents had a good level of overall healthcare providers' readiness for the adoption of EHR which was considered inadequate. This finding implied that a huge effort is required to improve readiness before the actual implementation of EHRs. The finding implied that younger-aged groups were more ready for such technology which in turn implied; the older one needs more concern. Enhancing computer literacy, confidence building to raise self-efficacy of such technology, addressing the issue of computer availability at health facilities, building a positive attitude, awareness campaign of EHR, and recognizing the usefulness of such systems were the necessary measures to improve EHR readiness in this setting. Additionally, further studies are recommended to encompass all types of EHR readiness such as organizational readiness, technology readiness, societal readiness, and so on. Additionally, exploring the healthcare provider opinion with qualitative study and extending the proposed study to other implementation settings are recommended to be addressed by future works.

Factors that Determine Comprehensive Categorical Classification of EHR Implementation Levels

Electronic Health Records (EHRs) have the potential to alleviate patient safety mistakes. Of the various levels of EHR, advanced or higher-level functionalities of EHR are designed to improve patient safety. Certain organizational and environmental factors may pose as barriers toward implementing all of the functionalities, leaving certain hospitals intermediate between basic and comprehensive levels of implementation. This study identifies a comprehensive categorical classification that includes hospitals that have functionalities between basic and comprehensive levels of EHR and determines the organizational and environmental factors that may influence hospitals to implement one or more combinations of these categories. A longitudinal panel design was used. Ordinal logistic regression with random effects model was fitted with robust cluster standard errors. Our sample consisted of non-federal general acute care hospitals utilizing a panel design from 2010 to 2016 with 17 586 hospital-year observations (or an average of 2600 hospitals per year). Larger size hospitals, ones with higher total margin, metropolitan and urban hospitals, system affiliated hospitals, and those in higher managed care penetration areas have higher odds of belonging in one of the higher categories of EHR implementation. Hospitals that can access a greater amount of human resources and financial assets from their environments, may implement higher levels of EHR. Initial and maintenance costs of EHR, interoperability issues, and inability to distribute high costs of training across facilities may stymie implementation of higher EHR functionalities. Policymaking to encourage competition among vendors may possibly lower the implementation price for hospitals with limited resources.

Fueling Clinical and Translational Research in Appalachia: Informatics Platform Approach

BACKGROUND

The Appalachian population is distinct, not just culturally and geographically but also in its health care needs, facing the most health care disparities in the United States. To meet these unique demands, Appalachian medical centers need an arsenal of analytics and data science tools with the foundation of a centralized data warehouse to transform health care data into actionable clinical interventions. However, this is an especially challenging task given the fragmented state of medical data within Appalachia and the need for integration of other types of data such as environmental, social, and economic with medical data.

OBJECTIVE

This paper aims to present the structure and process of the development of an integrated platform at a midlevel Appalachian academic medical center along with its initial uses.

METHODS

The Appalachian Informatics Platform was developed by the Appalachian Clinical and Translational Science Institute's Division of Clinical Informatics and consists of 4 major components: a centralized clinical data warehouse, modeling (statistical and machine learning), visualization, and model evaluation. Data from different clinical systems, billing systems, and state- or national-level data sets were integrated into a centralized data warehouse. The platform supports research efforts by enabling curation and analysis of data using the different components, as appropriate.

RESULTS

The Appalachian Informatics Platform is functional and has supported several research efforts since its implementation for a variety of purposes, such as increasing knowledge of the pathophysiology of diseases, risk identification, risk prediction, and health care resource utilization research and estimation of the economic impact of diseases.

CONCLUSIONS

The platform provides an inexpensive yet seamless way to translate clinical and translational research ideas into clinical applications for regions similar to Appalachia that have limited resources and a largely rural population.

Survey of psychiatrist use of digital technology in clinical practice

Background

Psychiatrists were surveyed to obtain an overview of how they currently use technology in clinical practice, with a focus on psychiatrists who treat patients with bipolar disorder.

Methods

Data were obtained using an online-only survey containing 46 questions, completed by a convenience sample of 209 psychiatrists in 19 countries. Descriptive statistics, and analyses of linear associations and to remove country heterogeneity were calculated.

Results

Virtually all psychiatrists seek information online with many benefits, but some experience information overload. 75.2% of psychiatrists use an EMR/EHR at work, and 64.6% communicate with patients using a new technology, primarily email (48.8%). 66.0% do not ask patients if they use the Internet in relation to bipolar disorder. 67.3% of psychiatrists feel it is too early to tell if patient online information seeking about bipolar disorder is improving the quality of care. 66.3% of psychiatrists think technology-based treatments will improve the quality of care for some or many patients. However, 60.0% of psychiatrists do not recommend technology-based treatments to patients, and those who recommend select a variety of treatments. Psychiatrists use technology more frequently when the patients live in urban rather than rural or suburban areas. Only 23.9% of psychiatrists have any formal training in technology.

Conclusions

Digital technology is routinely used by psychiatrists in clinical practice. There is near unanimous agreement about the benefits of psychiatrist online information-seeking, but research on information overload is needed. There is less agreement about the appropriate use of other clinical technologies, especially those involving patients. It is too early to tell if technology-based treatments or patient Internet activities will improve the quality of care. The digital divide remains between use of technology for psychiatrists with patients living in urban and rural or suburban areas. Psychiatrists need more formal training in technology to understand risks, benefits and limitations of clinical products.

Association of Electronic Health Record Use Above Meaningful Use Thresholds With Hospital Quality and Safety Outcomes

IMPORTANCE

By 2018, Medicare spent more than $30 billion to incentivize the adoption of electronic health records (EHRs), based partially on the belief that EHRs would improve health care quality and safety. In a time when most hospitals are well past minimum meaningful use (MU) requirements, examining whether EHR implementation beyond the minimum threshold is associated with increased quality and safety may guide the future focus of EHR development and incentive structures.

OBJECTIVE

To determine whether EHR implementation above MU performance thresholds is associated with changes in hospital patient satisfaction, efficiency, and safety.

DESIGN, SETTING, AND PARTICIPANTS

This quantile regression analysis of cross-sectional data used publicly available data sets from 2362 acute care hospitals in the United States participating in both the MU and Hospital Value-Based Purchasing (HVBP) programs from January 1 to December 31, 2016. Data were analyzed from August 1, 2019, to May 22, 2020.

EXPOSURES

Seven MU program performance measures, including medication and laboratory orders placed through the EHR, online health information availability and access rates, medication reconciliation through the EHR, patient-specific educational resources, and electronic health information exchange.

MAIN OUTCOMES AND MEASURES

The HVBP outcomes included patient satisfaction survey dimensions, Medicare spending per beneficiary, and 5 types of hospital-acquired infections.

RESULTS

Among the 2362 participating hospitals, mixed associations were found between MU measures and HVBP outcomes, all varying by outcome quantile and in some cases by interaction with EHR vendor. Computerized provider order entry (CPOE) for laboratory orders was associated with decreased ratings of every patient satisfaction outcome at middle quantiles (communication with nurses: β = -0.33 [P = .04]; communication with physicians: β = -0.50 [P < .001]; responsiveness of hospital staff: β = -0.57 [P = .03]; care transition performance: β = -0.66 [P < .001]; communication about medicines: β = -0.52 [P = .002]; cleanliness and quietness: β = -0.58 [P = .007]; discharge information: β = -0.48 [P < .001]; and overall rating: β = -0.95 [P < .001]). However, at middle quantiles, CPOE for medication orders was associated with increased ratings for communication with physicians (τ = 0.5; β = 0.54; P = .009), care transition (τ = 0.5; β = 1.24; P < .001), discharge information (τ = 0.5; β = 0.41; P = .01), and overall hospital ratings (τ = 0.5; β = 0.97; P = .02). At high quantiles, electronic health information exchange was associated with improved ratings of communication with nurses (τ = 0.9; β = 0.23; P = .03). Medication reconciliation had positive associations with increased communication with nursing at low quantiles (τ = 0.1; β = 0.60; P < .001), increased discharge information at middle quantiles (τ = 0.5; β = 0.28; P = .03), and responsiveness of hospital staff at middle (τ = 0.5; β = 0.77; P = .001) and high (τ = 0.9; β = 0.84; P = .001) quantiles. Patients accessing their health information online was not associated with any outcomes. Increased use of patient-specific educational resources identified through the EHR was associated with increased ratings of communication with physicians at high quantiles (τ = 0.9; β = 0.20; P = .02) and with decreased spending at low-spending hospitals (τ = 0.1; β = -0.40; P = .008).

CONCLUSIONS AND RELEVANCE

Increasing EHR implementation, as measured by MU criteria, was not straightforwardly associated with increased HVBP measures of patient satisfaction, spending, and safety in this study. These results call for a critical evaluation of the criteria by which EHR implementation is measured and increased attention to how different EHR products may lead to differential outcomes.

Rural-Urban Differences in In-Hospital Mortality Among Admissions for End-Stage Liver Disease in the United States

Access to quality hospital care is a persistent problem for rural patients. Little is known about disparities between rural and urban populations regarding in-hospital outcomes for end-stage liver disease (ESLD) patients. We aimed to determine whether rural ESLD patients experienced higher in-hospital mortality than urban patients and whether disparities were attributable to the rurality of the patient or the center. This was a retrospective study of patient admissions in the National Inpatient Sample, a population-based sample of hospitals in the United States. Admissions were included if they were from adult patients who had an ESLD-related admission defined by codes from the International Classification of Diseases, Ninth Revision, between January 2012 and December 2014. The primary exposures of interest were patient-level rurality and hospital-level rurality. The main outcome was in-hospital mortality. We stratified our analysis by disease severity score. After accounting for patient- and hospital-level covariates, ESLD admissions to rural hospitals in every category of disease severity had significantly higher odds of in-hospital mortality than patient admissions to urban hospitals. Those with moderate or major risk of dying had more than twice the odds of in-hospital mortality (odds ratio [OR] for moderate risk, 2.41; 95% confidence interval [CI], 1.62-3.59; OR for major risk, 2.49; 95% CI, 1.97-3.14). There was no association between patient-level rurality and mortality in the adjusted models. In conclusion, ESLD patients admitted to rural hospitals had increased odds of in-hospital mortality compared with those admitted to urban hospitals, and the differences were not attributable to patient-level rurality. Our results suggest that interventions to improve outcomes in this population should focus on the level of the health system.

An Assessment of Participant-Described Interprofessional Oral Health Referral Systems Across Rurality

PURPOSE

As a means to identify and quantify oral health interprofessional collaborative practice (IPP), we examined participant-described medical-to-dental (M2D) referral networks and how they function across rurality.

METHODS

We conducted a cross-sectional survey on the appraisal of IPP referral systems in 2016. Secondarily, we examined if rural health clinics (RHCs) have different experiences with M2D referrals compared to other practice types. Independent variables included geographic and organizational indicators, referral system attributes, and respondent characteristics. Data were coded by Census region and state Medicaid expansion status. Bivariable and multivariable analyses were conducted using SAS.

FINDINGS

A convenience cohort (n = 559) from 44 states was examined. Nearly, half (48.7%) reported dependable M2D referral systems. In bivariate analysis, all independent variables were significant except for state Medicaid expansion status. In multivariable analysis, Census region retained significance (P = .0093). Organization type and practice issues with no shows/missed appointments continued to have significance (P < .001 and .002, respectively). Accountable care organizations were over 5 times (5.72, P = .001) more likely than RHCs to report dependable M2D referral systems. Federally qualified health clinics were slightly over 3 times more likely than RHCs to report dependable M2D referral (3.04, P < .001). No differences between RHCs and other private practices were observed.

CONCLUSIONS

The importance of IPP continues to be promoted in the current health care environment. Our study demonstrates that, in this motivated study population, M2D referrals can work well, even in rural areas. Organization type, directionality of referral, broken appointment rates, and electronic health information management were all found to significantly impact the respondents' rating on the dependability of an M2D referral process.