Dual Role of IT-Assisted Communication in Patient Care: A Validated Structure-Process-Outcome Framework

Harnessing the Power of Complementarity Between Smart Tracking Technology and Associated Health Information Technologies: Longitudinal Study

BACKGROUND

Smart tracking technology (STT) that was applied for clinical use has the potential to reduce 30-day all-cause readmission risk through streamlining clinical workflows with improved accuracy, mobility, and efficiency. However, previously published literature has inadequately addressed the joint effects of STT for clinical use and its complementary health ITs (HITs) in this context. Furthermore, while previous studies have discussed the symbiotic and pooled complementarity effects among different HITs, there is a lack of evidence-based research specifically examining the complementarity effects between STT for clinical use and other relevant HITs.

OBJECTIVE

Through a complementarity theory lens, this study aims to examine the joint effects of STT for clinical use and 3 relevant HITs on 30-day all-cause readmission risk. These HITs are STT for supply chain management, mobile IT, and health information exchange (HIE). Specifically, this study examines whether the pooled complementarity effect exists between STT for clinical use and STT for supply chain management, and whether symbiotic complementarity effects exist between STT for clinical use and mobile IT and between STT for clinical use and HIE.

METHODS

This study uses a longitudinal in-patient dataset, including 879,122 in-patient hospital admissions for 347,949 patients in 61 hospitals located in Florida and New York in the United States, from 2014 to 2015. Logistic regression was applied to assess the effect of HITs on readmission risks. Time and hospital fixed effects were controlled in the regression model. Robust standard errors (SEs) were used to account for potential heteroskedasticity. These errors were further clustered at the patient level to consider possible correlations within the patient groups.

RESULTS

The interaction between STT for clinical use and STT for supply chain management, mobile IT, and HIE was negatively associated with 30-day readmission risk, with coefficients of -0.0352 (P=.003), -0.0520 (P<.001), and -0.0216 (P=.04), respectively. These results indicate that the pooled complementarity effect exists between STT for clinical use and STT for supply chain management, and symbiotic complementarity effects exist between STT for clinical use and mobile IT and between STT for clinical use and HIE. Furthermore, the joint effects of these HITs varied depending on the hospital affiliation and patients' disease types.

CONCLUSIONS

Our results reveal that while individual HIT implementations have varying impacts on 30-day readmission risk, their joint effects are often associated with a reduction in 30-day readmission risk. This study substantially contributes to HIT value literature by quantifying the complementarity effects among 4 different types of HITs: STT for clinical use, STT for supply chain management, mobile IT, and HIE. It further offers practical implications for hospitals to maximize the benefits of their complementary HITs in reducing the 30-day readmission risk in their respective care scenarios.

Bridge the gap caused by public health crises: medical humanization and communication skills build a psychological bond that satisfies patients

BACKGROUND

Patient satisfaction is an important outcome domain of patient-centered care. Medical humanization follows the patient-centered principle and provides a more holistic view to treat patients. The COVID-19 pandemic posed significant barriers to maintaining medical humanization. However, empirical study on the relationship between medical humanization and patient satisfaction is clearly absent.

OBJECTIVES

We examined the mediation effects of communication on the relationship between medical humanization and patient satisfaction when faced with a huge public health crisis like the COVID-19 pandemic, and the moderation effect of medical institutional trust on the mediation models.

METHODS

A cross-sectional survey study was performed. A final sample size of 1445 patients was surveyed on medical humanization, communication, patient satisfaction and medical institutional trust.

RESULTS

All correlations were significantly positive across the main variables (r = 0.35-0.67, p < 0.001 for all) except for medical institutional trust, which was negatively correlated with the medical humanization (r=-0.14, p < 0.001). Moderated mediation analysis showed that the indirect effect of medical humanization on patient satisfaction through communication was significant (b = 0.22, 95% CI: 0.18 ~ 0.25). Medical institutional trust significantly moderated the effect of medical humanization on patient satisfaction (b=-0.09, p < 0.001) and the effect of medical humanization on communication (b= -0.14, p < 0.001).

CONCLUSION

Medical humanization positively influence patient satisfaction, communication mediated the association between medical humanization and patient satisfaction, and medical institutional trust negatively moderated the effects of medical humanization on patient satisfaction and communication. These findings suggest that humanistic communication contributes to patient satisfaction in the face of a huge public health crisis, and patients' evaluation of satisfaction is also regulated by rational cognition.

How to detect fake online physician reviews: A deep learning approach

Objective

The COVID-19 pandemic has spurred an increased interest in online healthcare and a surge in usage of online healthcare platforms, leading to a proliferation of user-generated online physician reviews. Yet, distinguishing between genuine and fake reviews poses a significant challenge. This study aims to address the challenges delineated above by developing a reliable and effective fake review detection model leveraging deep learning approaches based on a fake review dataset tailored to the context of Chinese online medical platforms.

Methods

Inspired by prior research, this paper adopts a crowdsourcing approach to assemble the fake review dataset for Chinese online medical platforms. To develop the fake review detection models, classical machine learning models, along with deep learning models such as Convolutional Neural Network and Bidirectional Encoder Representations from Transformers, were applied.

Results

Our experimental deep learning model exhibited superior performance in identifying fake reviews on online medical platforms, achieving a precision of 98.36% and an F2-Score of 97.97%. Compared to the traditional machine learning models (i.e., logistic regression, support vector machine, random forest, ridge regression), this represents an 8.16% enhancement in precision and a 7.7% increase in F2-Score.

Conclusion

Overall, this study provides a valuable contribution toward the development of an effective fake physician review detection model for online medical platforms.

Impact of Hospital Characteristics and Governance Structure on the Adoption of Tracking Technologies for Clinical and Supply Chain Use: Longitudinal Study of US Hospitals

Background

Despite the increasing adoption rate of tracking technologies in hospitals in the United States, few empirical studies have examined the factors involved in such adoption within different use contexts (eg, clinical and supply chain use contexts). To date, no study has systematically examined how governance structures impact technology adoption in different use contexts in hospitals. Given that the hospital governance structure fundamentally governs health care workflows and operations, understanding its critical role provides a solid foundation from which to explore factors involved in the adoption of tracking technologies in hospitals.

Objective

This study aims to compare critical factors associated with the adoption of tracking technologies for clinical and supply chain uses and examine how governance structure types affect the adoption of tracking technologies in hospitals.

Methods

This study was conducted based on a comprehensive and longitudinal national census data set comprising 3623 unique hospitals across 50 states in the United States from 2012 to 2015. Using mixed effects population logistic regression models to account for the effects within and between hospitals, we captured and examined the effects of hospital characteristics, locations, and governance structure on adjustments to the innate development of tracking technology over time.

Results

From 2012 to 2015, we discovered that the proportion of hospitals in which tracking technologies were fully implemented for clinical use increased from 36.34% (782/2152) to 54.63% (1316/2409), and that for supply chain use increased from 28.58% (615/2152) to 41.3% (995/2409). We also discovered that adoption factors impact the clinical and supply chain use contexts differently. In the clinical use context, compared with hospitals located in urban areas, hospitals in rural areas (odds ratio [OR] 0.68, 95% CI 0.56-0.80) are less likely to fully adopt tracking technologies. In the context of supply chain use, the type of governance structure influences tracking technology adoption. Compared with hospitals not affiliated with a health system, implementation rates increased as hospitals affiliated with a more centralized health system—1.9-fold increase (OR 1.87, 95% CI 1.60-2.13) for decentralized or independent hospitals, 2.4-fold increase (OR 2.40, 95% CI 2.07-2.80) for moderately centralized health systems, and 3.1-fold increase for centralized health systems (OR 3.07, 95% CI 2.67-3.53).

Conclusions

As the first of such type of studies, we provided a longitudinal overview of how hospital characteristics and governance structure jointly affect adoption rates of tracking technology in both clinical and supply chain use contexts, which is essential for developing intelligent infrastructure for smart hospital systems. This study informs researchers, health care providers, and policy makers that hospital characteristics, locations, and governance structures have different impacts on the adoption of tracking technologies for clinical and supply chain use and on health resource disparities among hospitals of different sizes, locations, and governance structures.

Functional IT Complementarity and Hospital Performance in the United States: A Longitudinal Investigation

For healthcare practitioners and policymakers, one of the most challenging problems is understanding how to implement health information technology (HIT) applications in a way that yields the most positive impacts on quality and cost of care. We identify four clinical HIT functions which we label as order entry and management (OEM), decision support (DS), electronic clinical documentation (ECD), and results viewing (RV). We view OEM and DS as primary clinical functions and ECD and RV as support clinical functions. Our results show that no single combination of applications uniformly improves clinical and experiential quality and reduces cost for all hospitals. Thus, managers must assess which HIT interactions improve which performance metric under which conditions. Our results suggest that synergies can be realized when these systems are implemented simultaneously. Additionally, synergies can occur when support HIT is implemented before primary HIT and irrespective of the order in which primary HITs are implemented. Practitioners should also be aware that the synergistic effects of HITs and their impact on cost and quality are different for chronic and acute diseases. Our key message to top managers is to prioritize different combinations of HIT contingent on the performance variables they are targeting for their hospitals but also to realize that technology may not impact all outcomes.

Health Professionals' Perspectives on Electronic Medical Record Infusion and Individual Performance: Model Development and Questionnaire Survey Study

Background

Electronic medical records (EMRs) are integrated information sources generated by health care professionals (HCPs) from various health care information systems. EMRs play crucial roles in improving the quality of care and medical decision-making and in facilitating cross-hospital health information exchange. Although many hospitals have invested considerable resources and efforts to develop EMRs for several years, the factors affecting the long-term success of EMRs, particularly in the EMR infusion stage, remain unclear.

Objective

The aim of this study was to investigate the effects of technology, user, and task characteristics on EMR infusion to determine the factors that largely affect EMR infusion. In addition, we examined the effect of EMR infusion on individual HCP performance.

Methods

A questionnaire survey was used to collect data from HCPs with >6 months experience in using EMRs in a Taiwanese teaching hospital. A total of 316 questionnaires were distributed and 211 complete copies were returned, yielding a valid response rate of 66.8%. The collected data were further analyzed using WarpPLS 5.0.

Results

EMR infusion (R²=0.771) was mainly affected by user habits (β=.411), portability (β=.217), personal innovativeness (β=.198), technostress (β=.169), and time criticality (β=.168), and individual performance (R²=0.541) was affected by EMR infusion (β=.735). This finding indicated that user (habit, personal innovativeness, and technostress), technology (portability), and task (mobility and time criticality) characteristics have major effects on EMR infusion. Furthermore, the results indicated that EMR infusion positively affects individual performance.

Conclusions

The factors identified in this study can extend information systems infusion theory and provide useful insights for the further improvement of EMR development in hospitals and by the government, specifically in its infusion stage. In addition, the developed instrument can be used as an assessment tool to identify the key factors for EMR infusion, and to evaluate the extent of EMR infusion and the individual performance of hospitals that have implemented EMR systems. Moreover, the results can help governments to understand the urgent needs of hospitals in implementing EMR systems, provide sufficient resources and support to improve the incentives of EMR development, and develop adequate EMR policies for the meaningful use of electronic health records among hospitals and clinics.

Effectiveness of Digital Technologies to Support Nursing Care: Results of a Scoping Review

BACKGROUND

The field of digital technologies being developed or applied to support nursing care is extensive. The aim of this scoping review is to provide an overview on technologies for which results on positive or negative effects on persons in need of care, caregivers or care institutions are available and to appraise the reliability of these results.

METHODS

A scoping review design has been used to identify studies focussing on the effectiveness of digital technologies in nursing care for persons in need of care, caregivers or care institutions. The screening process included 19,510 scientific publications from 9 databases.

RESULTS

A total of 123 single studies and 31 reviews were subjected to the analysis. The included technologies comprise nursing and health information technologies, such as assistive devices, information and communication technologies, sensors and robotics. The results show that there are many studies that demonstrate positive effects, but the level of evidence is mostly low and study sizes are often small. Hardly any technology has been researched intensively enough to produce conclusive results. Studies on a high level of evidence (RCTs) lack for most technological areas. Heterogeneous results in some areas indicate that effects may depend strongly on the mode and specific context into which the technologies are introduced.

CONCLUSION

Due to the limited evidence on effectiveness of digital technologies in nursing care, it is not surprising that care institutions are reluctant to put innovative technologies into practice. The scoping review indicates technology areas that should be subject to future research with higher quality studies. Research on outpatient, informal and cross-sectoral care should be intensified to further exploit the potential of digital technologies with a view to improving independence of care-recipients and unburdening formal and informal carers.

Measuring the effectiveness of digital nursing technologies: development of a comprehensive digital nursing technology outcome framework based on a scoping review

BACKGROUND

Digital nursing technologies (DNT) comprise an expanding, highly diverse field of research, explored using a wide variety of methods and tools. Study results are therefore difficult to compare, which raises the question how effectiveness of DNT can be adequately measured. Methods currently used might not be sufficient for certain specific nursing contexts. A comprehensive outcome framework that shows the multitude of possible outcome areas could be useful to generate more comparable results. The aim of the present study is to develop an outcome framework for DNT and to indicate which outcome areas have been most frequently evaluated in previous studies and how this has been done.

METHODS

We combined an inductive and deductive approach to develop the framework. The numerical analysis is based on a scoping review focussing on the effectiveness of DNT for persons in need of care, formal or informal caregivers or care institutions. Nine databases were included in the screening: Medline, Scopus, CINAHL, Cochrane Library, ACM Digital Library, IEEE Xplore, the Collection of Computer Science Bibliographies, GeroLit and CareLit. Additional literature searches and expert interviews were included.

RESULTS

The developed framework comprises four outcome target groups and 47 outcome areas. There are considerable differences in the researched outcome areas for the individual outcome target groups. Persons in need of care were by far the most frequently surveyed, particularly with respect to their psychological health. There are much fewer studies on formal and informal caregivers, and it is particularly noticeable that the quality of life of both groups has rarely been investigated. Care process quality was most frequently researched for organisations.

CONCLUSION

We were able to provide a comprehensive DNT outcome framework, thereby identifying the outcome tools used and the less researched outcome areas. We recommend a detailed investigation of all areas and tools in future research projects with a view to initiating a discussion on the differing importance of existing outcome areas and on a standardisation of outcome tools. We also recommend the development of outcome areas for the macro level of effectiveness assessment.

The efficiency analysis and spatial implications of health information technology: A regional exploratory study in China

The new adoption of healthcare information technology is costly, and effects on healthcare performance can be questionable. This nationwide study in China investigated the efficient performance of healthcare information technology and examined its spatial correlation. Panel data were extracted from the Annual Investigation Report on Hospital Information in China and the China Health Statistics Yearbook for 2007 through 2015 (279 observations). Stochastic frontier analysis was employed to estimate the technical efficiency of healthcare information technology performance and related factors at the regional level. Healthcare information technology performance was positively associated with electronic medical records, total input, and cost of inpatient stay, while picture archiving and communication systems and net assets were negatively related. Local Indicators of Spatial Association showed that there existed significant spatial autocorrelation. Governmental policies would best make distinctions among different forms of healthcare information technology, especially between electronic medical records and picture archiving and communication systems. Policies should be formulated to improve healthcare information technology adoption and reduce regional differences.

The impact mechanism of the controlling system in hospitals on quality of care: A study on clinical practice in China

BACKGROUND

Quality control system is one of the hospital information systems. The adoption of quality control system increases the work efficiency; however, to some extent, it also increases the workload for physicians.

OBJECTIVE

The purpose of this study is to investigate the impacts of the quality control system on quality of care (e.g., process and outcome performance).

METHODS

Our study collected physicians' behavior information from a large urban hospital in China. We constructed the fixed-effect model to examine the relationship between the quality control system adoption and quality of care.

RESULTS

Using the quality control system has a significant (p< 0.001) and negative effect on patients' stay length in the hospital (process performance). Furthermore, using the quality control system has a significant (p< 0.001) and positive effect on the trends of cure rate in the hospital (outcome performance). The coefficient of the dependent variable from the patients' stay length (process performance) is lower than the trends of cure rate (outcome performance).

CONCLUSIONS

The controlling system can improve medical quality even though it limits physician behavior to some extent. The controlling system improves both the process performance and outcome performance, and it brings more benefits to outcome performance rather than process performance which means the reflection of the new technology may have more evident on outcome variables.

Exploring the Impact of the Prescription Automatic Screening System in Health Care Services: Quasi-Experiment

BACKGROUND

Hospitals have deployed various types of technologies to alleviate the problem of high medical costs. The cost of pharmaceuticals is one of the main drivers of medical costs. The Prescription Automatic Screening System (PASS) aims to monitor physicians' prescribing behavior, which has the potential to decrease prescription errors and medical treatment costs. However, a substantial number of cases with unsatisfactory results related to the effects of PASS have been noted.

OBJECTIVE

The objectives of this study were to systematically explore the imperative role of PASS on hospitals' prescription errors and medical treatment costs and examine its contingency factors to clarify the various factors associated with the effective use of PASS.

METHODS

To systematically examine the various effects of PASS, we adopted a quasi-experiment methodology by using a 2-year observation dataset from 2 hospitals in China. We then analyzed the data related to physicians' prescriptions both before and after the deployment of PASS and eliminated influences from a variety of perplexing factors by utilizing a control hospital that did not use a PASS system. In total, 754 physicians were included in this experiment comprising 11,054 patients: 400 physicians in the treatment group and 354 physicians in the control group. This study was also preceded by a series of interviews, which were employed to identify moderators. Thereafter, we adopted propensity score matching integrated with difference-in-differences to isolate the effects of PASS.

RESULTS

The effects of PASS on prescription errors and medical treatment costs were all significant (error: 95% CI -0.40 to -0.11, P=.001; costs: 95% CI -0.75 to -0.12, P=.007). Pressure from organizational rules and workload decreased the effect of PASS on prescription errors (95% CI 0.18-0.39; P<.001) and medical treatment costs (95% CI 0.07-0.55; P=.01), respectively. We also suspected that other pressures (eg, clinical title and risk categories of illness) also impaired physicians' attention to alerts from PASS. However, the effects of PASS did not change among physicians with a higher clinical title or when treating diseases demonstrating high risk. This may be attributed to the fact that these physicians will focus more on their patients in these situations, regardless of having access to an intelligent system.

CONCLUSIONS

Although implementation of PASS decreases prescription errors and medical treatment costs, workload and organizational rules remain problematic, as they tend to impair the positive effects of auxiliary diagnosis systems on performance. This again highlights the importance of considering both technical and organizational issues to obtain the highest level of effectiveness when deploying information technology in hospitals.

Investigating Knowledge Flows between Information Systems and Other Disciplines:

Information systems (IS) is one of the most rapidly changing disciplines in the social science field, and it is currently facing a new academic shift. The prevailing concepts, such as big data and Internet of things (IoT), imply that there is a plethora of research opportunities for IS researchers. Since these opportunities lie mostly in conjunction with other disciplines closely related to IS, it is essential to identify the interaction between IS and those disciplines. A few studies using bibliometric analysis have been published regarding this topic. However, we have identified several limitations in them: (i) inclusion of only a small journal basket, (ii) focus on a very restricted area of discipline, and (iii) a methodological limitation that can lead to the failure to capture the authentic knowledge flow between IS and other disciplines. We attempt to extend previous studies by proposing a comprehensive analysis model with the largest journal basket and areas of disciplines. As a result of our analysis, a knowledge flow structure different from that of past research is identified. In addition, through the discussion on emerging reference disciplines, we discover new research opportunities into which IS researchers can delve.

Evaluation of health information systems research in information systems research: A meta-analysis

Given the importance of the health-care industry and the promise of health information systems, researchers are encouraged to build on the shoulders of giants as the saying goes. The health information systems field has a unique opportunity to learn from and extend the work that has already been done by the highly correlated information systems field. As a result, this research article presents a past, present and future meta-analysis of health information systems research in information systems journals over the 2000–2015 time period. Our analysis reviewed 126 articles on a variety of topics related to health information systems research published in the “Senior Scholars” list of the top eight ranked information systems academic journals. Across the selected information systems academic journals, our findings compare research methodologies applied, health information systems topic areas investigated and research trends. Interesting results emerge in the range and evolution of health information systems research and opportunities for health information systems researchers and practitioners to consider moving forward.

The Influence of Remote Monitoring on Clinical Decision Making

The aim of this study was to examine the meaningful use of remote monitoring. In partnership with a home health care agency, we examined the degree to which the information obtained from remote monitoring influenced change in decisions that affect readmission rates among patients diagnosed with heart failure. Hospital utilization was highly associated with nurses’ clinical decisions to go to the hospital; the additional data provided by technology did not modify decisions. The role of nurses in heart failure management is critical to quality outcomes. Investments into remote monitoring technology should accompany strategies to enhance decision making and align clinical decision making with quality goals.