Validation of the Registered Nurse Assessment of Readiness for Hospital Discharge Scale

Background Statistical models for predicting readmissions have been published for high-risk patient populations but typically focus on patient characteristics; nurse judgment is rarely considered in a formalized way to supplement prediction models. Objectives The purpose of this study was to determine psychometric properties of long and short forms of the Registered Nurse Readiness for Hospital Discharge Scale (RN-RHDS), including reliability, factor structure, and predictive validity. Methods Data were aggregated from two studies conducted at four hospitals in the Midwestern United States. The RN-RHDS was completed within 4 hours before hospital discharge by the discharging nurse. Data on readmissions and emergency department visits within 30 days were extracted from electronic medical records. Results The RN-RHDS, both long and short forms, demonstrate acceptable reliability (Cronbach’s alphas of .90 and .73, respectively). Confirmatory factor analysis demonstrated less than adequate fit with the same four-factor structure observed in the patient version. Exploratory factor analysis identified three factors, explaining 60.2% of the variance. When nurses rate patients as less ready to go home (\u3c7 out of 10), patients are 6.4–9.3 times more likely to return to the hospital within 30 days, in adjusted models. Discussion The RN-RHDS, long and short forms, can be used to identify medical-surgical patients at risk for potential unplanned return to hospital within 30 days, allowing nurses to use their clinical judgment to implement interventions prior to discharge. Use of the RN-RHDS could enhance current readmission risk prediction models

Use of electronic medical records and biomarkers to manage risk and resource efficiencies

Peer reviewedPublisher PD

Building Data-Driven Pathways From Routinely Collected Hospital Data:A Case Study on Prostate Cancer

Background: Routinely collected data in hospitals is complex, typically heterogeneous, and scattered across multiple Hospital Information Systems (HIS). This big data, created as a byproduct of health care activities, has the potential to provide a better understanding of diseases, unearth hidden patterns, and improve services and cost. The extent and uses of such data rely on its quality, which is not consistently checked, nor fully understood. Nevertheless, using routine data for the construction of data-driven clinical pathways, describing processes and trends, is a key topic receiving increasing attention in the literature. Traditional algorithms do not cope well with unstructured processes or data, and do not produce clinically meaningful visualizations. Supporting systems that provide additional information, context, and quality assurance inspection are needed. Objective: The objective of the study is to explore how routine hospital data can be used to develop data-driven pathways that describe the journeys that patients take through care, and their potential uses in biomedical research; it proposes a framework for the construction, quality assessment, and visualization of patient pathways for clinical studies and decision support using a case study on prostate cancer. Methods: Data pertaining to prostate cancer patients were extracted from a large UK hospital from eight different HIS, validated, and complemented with information from the local cancer registry. Data-driven pathways were built for each of the 1904 patients and an expert knowledge base, containing rules on the prostate cancer biomarker, was used to assess the completeness and utility of the pathways for a specific clinical study. Software components were built to provide meaningful visualizations for the constructed pathways. Results: The proposed framework and pathway formalism enable the summarization, visualization, and querying of complex patient-centric clinical information, as well as the computation of quality indicators and dimensions. A novel graphical representation of the pathways allows the synthesis of such information. Conclusions: Clinical pathways built from routinely collected hospital data can unearth information about patients and diseases that may otherwise be unavailable or overlooked in hospitals. Data-driven clinical pathways allow for heterogeneous data (ie, semistructured and unstructured data) to be collated over a unified data model and for data quality dimensions to be assessed. This work has enabled further research on prostate cancer and its biomarkers, and on the development and application of methods to mine, compare, analyze, and visualize pathways constructed from routine data. This is an important development for the reuse of big data in hospitals

GP perspectives on hospital discharge letters : an interview and focus group study

Background: Written discharge communication following inpatient or outpatient clinic discharge is essential for communicating information to the GP, but GPs’ opinions on discharge communication are seldom sought. Patients are sometimes copied into this communication, but the reasons for this variation, and the resultant effects, remain unclear. Aim: To explore GP perspectives on how discharge letters can be improved in order to enhance patient outcomes. Design & setting: The study used narrative interviews with 26 GPs from 13 GP practices within the West Midlands, England. Method: Interviews were transcribed and data were analysed using corpus linguistics (CL) techniques. Results Elements pivotal to a successful letter were: diagnosis, appropriate follow-up plan, medication changes and reasons, clinical summary, investigations and/or procedures and outcomes, and what information has been given to the patient. GPs supported patients receiving discharge letters and expounded a number of benefits of this practice; for example, increased patient autonomy. Nevertheless, GPs felt that if patients are to receive direct discharge letter copies, modifications such as use of lay language and avoidance of acronyms may be required to increase patient understanding. Conclusion: GPs reported that discharge letters frequently lacked content items they assessed to be important; GPs highlighted that this can have subsequent ramifications on resources and patient experiences. Templates should be devised that put discharge letter elements assessed to be important by GPs to the forefront. Future research needs to consider other perspectives on letter content, particularly those of patients

Completeness of information in electronic compared with paper-based patients’ records in a maternity setting in Dakar, Senegal

Background: Evaluate the consistency of information in paper-based records when registered in parallel with an electronic medical record.Methods: The study was performed at PMSHC in Dakar Senegal. From the end of year 2016, patients’ files were recorded on both paper-based and electronically. Additionally, previous records were electronically registered. To investigate the completeness of records before and after the electronic recording system has been implemented, information about some maternal and fetal/neonatal characteristics were assessed. When the variable was recorded, the system returned 1, unrecorded variables were coded as 0. We then calculated, for each variable, the unrecorded rate before 2017 and after that date. The study period extended from 2011 to June 2019, a nearly ten-year period. Data were extracted from E-perinatal to MS excel 2019 then SPSS 25 software. Frequencies of unrecorded variables were compared with chi-squared test at a level of significance of 5%.Results: A total of 48,270 unique patients’ records were identified during the eight-year period.  Among the study population, data for patients’ age, address and parity were available most of the time before and after 2017 (0.5% missing data versus 0.3% for age and 2.6% versus 1.3% for home address and from 0.3% to 0.0% for parity). However, phone number, maternal weight, maternal height, last menstrual period and blood group were found to be missing up to 96% before 2017. From 2017, these rates experienced a sudden decrease at a significant level: from 82.4% to 27.8% for phone number, from 96% to 56.3% for maternal weight and from 60.1% to 21.3% for blood group. Regarding newborns’ data, it was found that fetal height, head circumference and chest circumference were missing up to just under 25% before 2017. After that date, their completeness improved and flattened under 5%.Conclusions: Structured and computerized files reduce missing data. There is an urgent need the Ministry of health provides hospitals and health care providers with guidelines that describes the standardized information that should be gathered and shared in health and care records

Equity in the Digital Age: How Health Information Technology Can Reduce Disparities

While enormous medical and technological advancements have been made over the last century, it is only very recently that there have been similar rates of development in the field of health information technology (HIT).This report examines some of the advancements in HIT and its potential to shape the future health care experiences of consumers. Combined with better data collection, HIT offers signi?cant opportunities to improve access to care, enhance health care quality, and create targeted strategies that help promote health equity. We must also keep in mind that technology gaps exist, particularly among communities of color, immigrants, and people who do not speak English well. HIT implementation must be done in a manner that responds to the needs of all populations to make sure that it enhances access, facilitates enrollment, and improves quality in a way that does not exacerbate existing health disparities for the most marginalized and underserved

A methodological framework for assessing agreement between cost-effectiveness outcomes estimated using alternative sources of data on treatment costs and effects for trial-based economic evaluations

A new methodological framework for assessing agreement between cost-effectiveness endpoints generated using alternative sources of data on treatment costs and effects for trial-based economic evaluations is proposed. The framework can be used to validate cost-effectiveness endpoints generated from routine data sources when comparable data is available directly from trial case report forms or from another source. We illustrate application of the framework using data from a recent trial-based economic evaluation of the probiotic Bifidobacterium breve strain BBG administered to babies less than 31 weeks of gestation. Cost-effectiveness endpoints are compared using two sources of information; trial case report forms and data extracted from the National Neonatal Research Database (NNRD), a clinical database created through collaborative efforts of UK neonatal services. Focusing on mean incremental net benefits at £30,000 per episode of sepsis averted, the study revealed no evidence of discrepancy between the data sources (two-sided p values >0.4), low probability estimates of miscoverage (ranging from 0.039 to 0.060) and concordance correlation coefficients greater than 0.86. We conclude that the NNRD could potentially serve as a reliable source of data for future trial-based economic evaluations of neonatal interventions. We also discuss the potential implications of increasing opportunity to utilize routinely available data for the conduct of trial-based economic evaluations

A methodological framework for assessing agreement between cost-effectiveness outcomes estimated using alternative sources of data on treatment costs and effects for trial-based economic evaluations

A new methodological framework for assessing agreement between cost-effectiveness endpoints generated using alternative sources of data on treatment costs and effects for trial-based economic evaluations is proposed. The framework can be used to validate cost-effectiveness endpoints generated from routine data sources when comparable data is available directly from trial case report forms or from another source. We illustrate application of the framework using data from a recent trial-based economic evaluation of the probiotic Bifidobacterium breve strain BBG administered to babies less than 31 weeks of gestation. Cost-effectiveness endpoints are compared using two sources of information; trial case report forms and data extracted from the National Neonatal Research Database (NNRD), a clinical database created through collaborative efforts of UK neonatal services. Focusing on mean incremental net benefits at £30,000 per episode of sepsis averted, the study revealed no evidence of discrepancy between the data sources (two-sided p values >0.4), low probability estimates of miscoverage (ranging from 0.039 to 0.060) and concordance correlation coefficients greater than 0.86. We conclude that the NNRD could potentially serve as a reliable source of data for future trial-based economic evaluations of neonatal interventions. We also discuss the potential implications of increasing opportunity to utilize routinely available data for the conduct of trial-based economic evaluations