Clinical Information System Availability and Use in Urban and Rural Hospitals

The Role of Artificial Intelligence in Clinical Decision Support Systems and a Classification Framework

Clinical decision support systems are meant to improve the quality of decision-making in healthcare. Artificial intelligence is the science of creating intelligent systems that solve complex problems at the level of or better than human experts. Combining artificial intelligence methods into clinical decision support will enable the utilization of large quantities of data to produce relevant decision-making information to practitioners. This article examines various artificial intelligence methodologies and shows how they may be incorporated into clinical decision-making systems. A framework for describing artificial intelligence applications in clinical decision support systems is presented.

Adoption of Electronic Dental Records: Examining the Influence of Practice Characteristics on Adoption in One State

OBJECTIVE

Compared with medicine, less research has focused on adoption rates and factors contributing to the adoption of electronic dental records (EDRs) and certified electronic health records (EHRs) in the field of dentistry. We ran two multivariate models on EDR adoption and certification-capable EHR adoption to determine environmental and organizational factors associated with adoption.

METHODS

We conducted telephone survey of a 10-item questionnaire using disproportionate stratified sampling procedure of 149 dental clinics in Tennessee in 2017 measuring adoption of dental information technology (IT) (EDRs and certification-capable EHRs) and practice characteristics, including region, rurality, specialty, and practice size. We used binomial logistic regression models to determine associations of adoption with predictor variables.

RESULTS

A total of 77% of surveyed dental clinics in Tennessee had adopted some type of EDR system. When the definitions of certification capable were applied, the adoption rates in dental clinics dropped to 58%. A binomial logistic regression model for the effects of rurality, specialization, and practice size on the likelihood that a clinic would adopt an EHR product was statistically significant (chi-square (3) = 12.41, p = 0.0061). Of the three predictor variables, specialization and practice size were significant: Odds of adopting an EHR is 67% lower for specialists than for general dentists; and clinics with two or more practicing dentists were associated with a much greater likelihood of adopting an EHR-capable system (adjusted odds ratio = 3.09, p = 0.009).

CONCLUSION

Findings from this study indicate moderate to high levels of overall dental IT adoption. However, adoption rates in dental clinics do remain lower than those observed in office-based physician practices in medicine. Specialization and practice size were significant predictors of EHR-capable system adoption. Efforts to increase EHR adoption in dentistry should be mindful of potential disparities in smaller practices and between dental specialties and generalists.

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

PURPOSE

To examine the difference between rural and urban hospitals as to their overall level of readiness for stage 2 meaningful use of electronic health records (EHRs) and to identify other key factors that affect their readiness for stage 2 meaningful use.

METHODS

A conceptual framework based on the theory of organizational readiness for change was used in a cross-sectional multivariate analysis using 2,083 samples drawn from the HIMSS Analytics survey conducted with US hospitals in 2013.

FINDINGS

Rural hospitals were less likely to be ready for stage 2 meaningful use compared to urban hospitals in the United States (OR = 0.49) in our final model. Hospitals' past experience with an information exchange initiative, staff size in the information system department, and the Chief Information Officer (CIO)'s responsibility for health information management were identified as the most critical organizational contextual factors that were associated with hospitals' readiness for stage 2. Rural hospitals lag behind urban hospitals in EHR adoption, which will hinder the interoperability of EHRs among providers across the nation. The identification of critical factors that relate to the adoption of EHR systems provides insights into possible organizational change efforts that can help hospitals to succeed in attaining meaningful use requirements.

CONCLUSION

Rural hospitals have increasingly limited resources, which have resulted in a struggle for these facilities to attain meaningful use. Given increasing closures among rural hospitals, it is all the more important that EHR development focus on advancing rural hospital quality of care and linkages with patients and other organizations supporting the care of their patients.

Lower rates of EMR use in rural hospitals represent a previously unexplored child health disparity

OBJECTIVES

Rural hospitals face significant barriers to adoption of advanced-stage electronic medical records (EMRs), which may translate to an unexplored disparity for children in rural hospitals. Our objective was to determine whether children hospitalized in rural settings are less likely to be cared for using advanced-stage EMRs.

METHODS

We merged the 2009 Healthcare Cost and Utilization Project Kids Inpatient Dataset with the 2009 Healthcare Information and Management Systems Society database. Logistic regression determined the independent relationship between receiving care in a rural hospital and advanced-stage EMRs.

RESULTS

A total of 430 055 (9.3%) of the 4 605 454 pediatric discharges were rural. Logistic regression analysis determined that even when an extensive list of various patient and hospital characteristics are accounted for, rurality continues to be a strong predictor of a child's care without advanced-stage EMRs (odds ratio 0.3; 95% confidence interval, 0.2-0.5).

CONCLUSIONS

Children hospitalized in a rural hospital are less than half as likely to be treated using advanced-stage EMRs. A focus of government and hospital policies to expand the use of EMRs among rural hospitals may reduce this child health care disparity.

Organizational factors influencing health information technology adoption in long-term-care facilities

Long-term care (LTC) is an important sector of the health care industry. However, the adoption of health information technology (HIT) systems in LTC facilities lags behind that in other sectors of health care. Previous literature has focused on the financial and technical barriers. This study examined the organizational factors associated with HIT adoption in LTC facilities. A survey of 500 LTC facilities in Texas enabled researchers to compile HIT indexes for further statistical analyses. A general linear model was used to study the associations between the clinical/administrative HIT indexes and organizational factors. The empirical outcomes show that the size of an LTC facility has a significant association with HIT adoption. Rural LTC facilities, especially freestanding ones, adopt less HIT than their urban counterparts, whereas freestanding LTC facilities have the lowest HIT adoption overall. There is not enough evidence to support ownership status as a significant factor in HIT adoption. Some implications are proposed, but further research is necessary.

Hospital IT Sophistication Profiles and Patient Safety Outcomes

Information technology (IT) sophistication of acute care hospitals in Texas was measured to explore the relationships between IT infrastructure and patient outcomes using Donabedian’s framework. The sample was acute care hospitals (n=175) with an IT profile using HIMSS, demographic and operations data. Three dimensions of hospital IT sophistication were measured and related to patient care outcomes using the AHRQ Patient Safety Indicators (PSI). Significant relationships (p < 0.05) using linear regression were found between hospital IT sophistication and three PSI measures. A review of similar studies during the same time period in Iowa, Georgia, and Florida compares findings from two instruments used to profile hospital IT infrastructure. This study adds to and confirms findings of positive relationships between IT sophistication of hospitals and patient care outcomes using the AHRQ safety indicators. Discussion of the conceptual model and the IT sophistication construct provides a theoretical framework for this line of research.

The impact of geography on hospital electronic health records implementation in alabama: implications for meaningful use

BACKGROUND

Given relatively less favorable health outcomes in rural Alabama, electronic health records (EHRs) have an even greater potential to improve quality and alleviate disparities if meaningfully used.

OBJECTIVES

We examined rural-urban differences as it pertained to perceived barriers, benefits, and motivating factors of EHR implementation.

METHODS

We used multivariate logistic models to analyze data collected from a state-wide, self-completed survey of health information management directors in Alabama hospitals.

RESULTS

Findings from our analyses indicate that fewer rural hospitals (8%) have implemented EHRs as compared with urban hospitals (18%). Rural hospitals were 71% less likely to consider reduction in costs as a benefit of EHRs (OR = 0.29), and were 75% less likely to consider lack of structured technology as a challenge factor of EHR implementation (OR = 0.25).

CONCLUSION

Promotion of EHRs in rural areas is challenging but necessary. Understanding perceived barriers and motivating factors of EHR implementation among rural hospitals can inform policy decisions, especially in light of recent meaningful use initiatives.

The adoption and use of health information technology in rural areas: results of a national survey

CONTEXT

Health information technology (HIT) is a national policy priority. Knowledge about the special needs, if any, of rural health care providers should be taken into account as policy is put into action. Little is known, however, about rural-urban differences in HIT adoption at the national level.

PURPOSE

To conduct the first national assessment of HIT in rural primary care offices, with particular attention to electronic medical record (EMR) adoption, range of capabilities in use, and plans for adoption.

METHODS

A national mail survey of 5,200 primary care offices, stratified by rurality using Rural-Urban Commuting Area categories, was conducted in 2007-2008. Regression analyses were used to assess the relationship between office characteristics and EMR adoption, capabilities used, and future adoption plans.

RESULTS

A commercial EMR system was present in 31% of offices, with no significant differences by rurality. Of offices with EMRs, 12% reported using a full range of EMR capabilities, with 51% using a basic range and 37% using less than the basic range. Large Rural (adjusted OR = 3.71, P= .022) and Small Rural (aOR = 3.75, P= .049) offices were more likely than Urban offices to use a broader range of EMR capabilities. Among offices without EMRs, those in Isolated areas were less likely to have more immediate plans to adopt (aOR = 0.19, P= .02).

CONCLUSIONS

HIT adoption and use in rural primary care offices does not appear to be lower than in urban offices. The situation, however, is dynamic and warrants further monitoring.

Evaluating clinical decision support rules as an intervention in clinician workflows with technology

The implementation of electronic health records in rural settings generated new challenges beyond those seen in urban hospitals. The preparation, implementation, and sustaining of clinical decision support rules require extensive attention to standards, content design, support resources, expert knowledge, and more. A formative evaluation was used to present progress and evolution of clinical decision support rule implementation and use within clinician workflows for application in an electronic health record. The rural hospital was able to use clinical decision support rules from five urban hospitals within its system to promote safety, prevent errors, establish evidence-based practices, and support communication. This article describes tools to validate initial 54 clinical decision support rules used in a rural referral hospital and 17 used in clinics. Since 2005, the study hospital has added specific system clinical decision support rules for catheter-acquired urinary tract infection, deep venous thrombosis, heart failure, and more. The findings validate the use of clinical decision support rules across sites and ability to use existing indicators to measure outcomes. Rural hospitals can rapidly overcome the barriers to prepare and implement as well as sustain use of clinical decision support rules with a systemized approach and support structures. A model for design and validation of clinical decision support rules into workflow processes is presented. The replication and reuse of clinical decision support rule templates with data specifications that follow data models can support reapplication of the rule intervention in subsequent rural and critical access hospitals through system support resources.

A regional assessment of information technology sophistication in Missouri nursing homes

OBJECTIVE

To provide a state profile of information technology (IT) sophistication in Missouri nursing homes.

METHOD

Primary survey data were collected from December 2006 to August 2007. A descriptive, exploratory cross-sectional design was used to investigate dimensions of IT sophistication (technological, functional, and integration) related to resident care, clinical support, and administrative processes. Each dimension was used to describe the clinical domains and demographics (ownership, regional location, and bed size).

RESULTS

The final sample included 185 nursing homes. A wide range of IT sophistication is being used in administrative and resident care management processes, but very little in clinical support activities.

CONCLUSION

Evidence suggests nursing homes in Missouri are expanding use of IT beyond traditional administrative and billing applications to patient care and clinical applications. This trend is important to provide support for capabilities which have been implemented to achieve national initiatives for meaningful use of IT in health care settings.

A network collaboration implementing technology to improve medication dispensing and administration in critical access hospitals

We report how seven independent critical access hospitals collaborated with a rural referral hospital to standardize workflow policies and procedures while jointly implementing the same health information technologies (HITs) to enhance medication care processes. The study hospitals implemented the same electronic health record, computerized provider order entry, pharmacy information systems, automated dispensing cabinets (ADC), and barcode medication administration systems. We conducted interviews and examined project documents to explore factors underlying the successful implementation of ADC and barcode medication administration across the network hospitals. These included a shared culture of collaboration; strategic sequencing of HIT component implementation; interface among HIT components; strategic placement of ADCs; disciplined use and sharing of workflow analyses linked with HIT applications; planning for workflow efficiencies; acquisition of adequate supply of HIT-related devices; and establishing metrics to monitor HIT use and outcomes.

Rural Health Clinic efficiency and effectiveness: insight from a nationwide survey

This study reports the results of a nationwide survey of Rural Health Clinics (RHCs). The purpose was to identify factors that contribute to efficiency and effectiveness in RHCs. Factors related to cost efficiency were analyzed using multiple regression; factors related to the likelihood of providing preventive diabetic care, an effectiveness indicator, were analyzed using logistic regression. The study found: (1) technical efficiency to be positively related to cost efficiency; (2) non-profit control to be inversely related to cost efficiency in independent RHCs; and (3) provider-based RHCs and technology use to be related to the likelihood of providing preventive diabetic care. Implications for RHCs are: (1) improvement in technical efficiency could enhance cost efficiency; (2) visits to PAs and NPs, an indicator of process efficiency, may not guarantee the provision of preventive diabetic care; and (3) strategies for improving RHC efficiency and effectiveness may be different for provider-based and independent clinics.

Medical image on the go!

The idea for softcopy viewing of medical image outside the radiology reading room spread among the scientists in various fields for several years. An image could be read on workstation of all types, from desktop across movable to handheld. Benefits are numerous and continue to grow as physicians use them discovering new usage cases. Proposed solutions vary with PACS architecture invasion level, communication and storage image formats, and utilization. We employ JPEG2000 standard because of its high (lossy/lossless) compression ratio with minimal spatial distortion, retrieval-oriented storage, and streaming. It is embedded in PACS as the DICOM Private Data Element containing JPIP parameter string, so-called DICOM2000. The DICOM2000 message is transparent for standard DICOM devices at the slightest level of invasion. Thanks to sophisticated JPEG2000 streaming, medical image becomes suitable for any resolution and quality display and (wireless) networks. The solution is validated on the ACR/NEMA standard test set of PACS images.

Information technology capacities assessment tool in hospitals: Instrument development and validation

Objectives:This research integrates existing literature on information technology (IT) in hospitals, and proposes and validates a comprehensive IT capacities assessment tool in these settings.

Methods:A comprehensive literature review was conducted on Medline until September 2006 to identify studies that used specific IT measures in hospitals. The results were mapped and used as a basis for the development of the proposed instrument, which was tested through a survey of Canadian healthcare organizations (N= 221).

Results:A total of seventeen studies provided indicators of clinical and administrative IT capacities in hospitals. Based on the mapping of these indicators, a comprehensive IT capacities assessment instrument was developed including thirty-four items exploring computerized processes, thirteen items assessing contemporary technologies, and eleven items investigating internal and external information sharing. A time frame was inserted in the tool to reflect “plans for” versus “current” implementation of IT; in the latter, the extent of current use of computerized processes and technologies was measured on a (1–7) scale. Overall, the survey yielded a total of 106 responses (52.2 percent response rate), and the results demonstrated a good level of reliability and validity of the instrument.

Conclusions:This study unifies existing work in this area, and presents the psychometric properties of an IT capacities assessment tool in hospitals. By developing scores for capturing IT capacities in hospitals, it is possible to further address important research questions related to the determinants and impacts of IT sophistication in these settings.