Challenges Associated With Using Large Data Sets for Quality Assessment and Research in Clinical Settings

Perspectives on the future of host-microbe biology from the Council on Microbial Sciences of the American Society for Microbiology

Host-microbe biology (HMB) stands on the cusp of redefinition, challenging conventional paradigms to instead embrace a more holistic understanding of the microbial sciences. The American Society for Microbiology (ASM) Council on Microbial Sciences hosted a virtual retreat in 2023 to identify the future of the HMB field and innovations needed to advance the microbial sciences. The retreat presentations and discussions collectively emphasized the interconnectedness of microbes and their profound influence on humans, animals, and environmental health, as well as the need to broaden perspectives to fully embrace the complexity of these interactions. To advance HMB research, microbial scientists would benefit from enhancing interdisciplinary and transdisciplinary research to utilize expertise in diverse fields, integrate different disciplines, and promote equity and accessibility within HMB. Data integration will be pivotal in shaping the future of HMB research by bringing together varied scientific perspectives, new and innovative techniques, and 'omics approaches. ASM can empower under-resourced groups with the goal of ensuring that the benefits of cutting-edge research reach every corner of the scientific community. Thus, ASM will be poised to steer HMB toward a future that champions inclusivity, innovation, and accessible scientific progress.

Beyond Hospital-Level Aggregated Data: A Methodology to Adapt Clinical Data From the Electronic Health Record for Nursing Unit-Level Research

BACKGROUND

Studies of nurse staffing frequently use data aggregated at the hospital level that do not provide the appropriate context to inform unit-level decisions, such as nurse staffing.

OBJECTIVES

Describe a method to link patient data collected during the provision of routine care and recorded in the electronic health record (EHR) to the nursing units where care occurred in a national dataset.

RESEARCH DESIGN

We identified all Veterans Health Administration acute care hospitalizations in the calendar year 2019 nationwide. We linked patient-level EHR and bar code medication administration data to nursing units using a crosswalk. We divided hospitalizations into segments based on the patient's time-stamped location (ward stays). We calculated the number of ward stays and medication administrations linked to a nursing unit and the unit-level and facility-level mean patient risk scores.

RESULTS

We extracted data on 1117 nursing units, 3782 EHR patient locations associated with 1,137,391 ward stays, and 67,772 bar code medication administration locations associated with 147,686,996 medication administrations across 125 Veterans Health Administration facilities. We linked 89.46% of ward stays and 93.10% of medication administrations to a nursing unit. The average (standard deviation) unit-level patient severity across all facilities is 4.71 (1.52), versus 4.53 (0.88) at the facility level.

CONCLUSIONS

Identification of units is indispensable for using EHR data to understand unit-level phenomena in nursing research and can provide the context-specific information needed by managers making frontline decisions about staffing.

Digital Health Data Quality Issues: Systematic Review

BACKGROUND

The promise of digital health is principally dependent on the ability to electronically capture data that can be analyzed to improve decision-making. However, the ability to effectively harness data has proven elusive, largely because of the quality of the data captured. Despite the importance of data quality (DQ), an agreed-upon DQ taxonomy evades literature. When consolidated frameworks are developed, the dimensions are often fragmented, without consideration of the interrelationships among the dimensions or their resultant impact.

OBJECTIVE

The aim of this study was to develop a consolidated digital health DQ dimension and outcome (DQ-DO) framework to provide insights into 3 research questions: What are the dimensions of digital health DQ? How are the dimensions of digital health DQ related? and What are the impacts of digital health DQ?

METHODS

Following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, a developmental systematic literature review was conducted of peer-reviewed literature focusing on digital health DQ in predominately hospital settings. A total of 227 relevant articles were retrieved and inductively analyzed to identify digital health DQ dimensions and outcomes. The inductive analysis was performed through open coding, constant comparison, and card sorting with subject matter experts to identify digital health DQ dimensions and digital health DQ outcomes. Subsequently, a computer-assisted analysis was performed and verified by DQ experts to identify the interrelationships among the DQ dimensions and relationships between DQ dimensions and outcomes. The analysis resulted in the development of the DQ-DO framework.

RESULTS

The digital health DQ-DO framework consists of 6 dimensions of DQ, namely accessibility, accuracy, completeness, consistency, contextual validity, and currency; interrelationships among the dimensions of digital health DQ, with consistency being the most influential dimension impacting all other digital health DQ dimensions; 5 digital health DQ outcomes, namely clinical, clinician, research-related, business process, and organizational outcomes; and relationships between the digital health DQ dimensions and DQ outcomes, with the consistency and accessibility dimensions impacting all DQ outcomes.

CONCLUSIONS

The DQ-DO framework developed in this study demonstrates the complexity of digital health DQ and the necessity for reducing digital health DQ issues. The framework further provides health care executives with holistic insights into DQ issues and resultant outcomes, which can help them prioritize which DQ-related problems to tackle first.

Design and Implementation of a Multipurpose Information System for Hematopoietic Stem-Cell Transplantation on the Basis of the Biomedical Research Integrated Domain Group Model

PURPOSE

An important obstacle to cancer research is that nearly all academic cancer centers maintain substantial collections of highly duplicative, poorly quality-assured, nonintercommunicating, difficult-to-access data repositories. It is inherently clear that this state of affairs increases costs and reduces quality and productivity of both research and nonresearch activities. We hypothesized that designing and implementing a multipurpose cancer information system on the basis of the Biomedical Research Integrated Domain (BRIDG) model developed by the National Cancer Institute and its collaborators might lessen the duplication of effort inherent in capturing, quality-assuring, and accessing data located in multiple single-purpose systems, and thereby increases productivity while reducing costs.

METHODS

We designed and implemented a core data structure on the basis of the BRIDG model and incorporated multiple entities, attributes, and functionalities to support the multipurpose functionality of the system. We used the resultant model as a foundation upon which to design and implement modules for importing preexisting data, capturing data prospectively, quality-assuring data, exporting data to analytic files, and analyzing the quality-assured data to support multiple functionalities simultaneously. To our knowledge, our system, which we refer to as the Cancer Informatics Data System, is the first multipurpose, BRIDG-harmonized cancer research information system implemented at an academic cancer center.

RESULTS

We describe the BRIDG-harmonized system that simultaneously supports patient care, teaching, research, clinical decision making, administrative decision making, mandated volume-and-outcomes reporting, clinical quality assurance, data quality assurance, and many other functionalities.

CONCLUSION

Implementation of a highly quality-assured, multipurpose cancer information system on the basis of the BRIDG model at an academic center is feasible and can increase access to accurate data to support research integrity and productivity as well as nonresearch activities.

PRIME-IPD SERIES Part 1. The PRIME-IPD tool promoted verification and standardization of study datasets retrieved for IPD meta-analysis

OBJECTIVES

We describe a systematic approach to preparing data in the conduct of Individual Participant Data (IPD) analysis.

STUDY DESIGN AND SETTING

A guidance paper proposing methods for preparing individual participant data for meta-analysis from multiple study sources, developed by consultation of relevant guidance and experts in IPD. We present an example of how these steps were applied in checking data for our own IPD meta analysis (IPD-MA).

RESULTS

We propose five steps of Processing, Replication, Imputation, Merging, and Evaluation to prepare individual participant data for meta-analysis (PRIME-IPD). Using our own IPD-MA as an exemplar, we found that this approach identified missing variables and potential inconsistencies in the data, facilitated the standardization of indicators across studies, confirmed that the correct data were received from investigators, and resulted in a single, verified dataset for IPD-MA.

CONCLUSION

The PRIME-IPD approach can assist researchers to systematically prepare, manage and conduct important quality checks on IPD from multiple studies for meta-analyses. Further testing of this framework in IPD-MA would be useful to refine these steps.

A likelihood-based convolution approach to estimate major health events in longitudinal health records data: an external validation study

OBJECTIVE

In electronic health record data, the exact time stamp of major health events, defined by significant physiologic or treatment changes, is often missing. We developed and externally validated a method that can accurately estimate these time stamps based on accurate time stamps of related data elements.

MATERIALS AND METHODS

A novel convolution-based change detection methodology was developed and tested using data from the national deidentified clinical claims OptumLabs data warehouse, then externally validated on a single center dataset derived from the M Health Fairview system.

RESULTS

We applied the methodology to estimate time to liver transplantation for waitlisted candidates. The median error between estimated date within the period of the actual true date was zero days, and median error was 92% and 84% of the transplants, in development and validation samples, respectively.

DISCUSSION

The proposed method can accurately estimate missing time stamps. Successful external validation suggests that the proposed method does not need to be refit to each health system; thus, it can be applied even when training data at the health system is insufficient or unavailable. The proposed method was applied to liver transplantation but can be more generally applied to any missing event that is accompanied by multiple related events that have accurate time stamps.

CONCLUSION

Missing time stamps in electronic healthcare record data can be estimated using time stamps of related events. Since the model was developed on a nationally representative dataset, it could be successfully transferred to a local health system without substantial loss of accuracy.

Toolkit for Best Practice Use of Electronic Health Record Data in Quality Improvement

This project piloted an educational intervention focused on use and management of EHR data by Doctor of Nursing Practice students in quality improvement initiatives. Recommendations from academic and clinical nursing promote the integration of EHR data findings into practice. Nursing's general lack of understanding about how to use and manage data is a barrier to using EHR data to guide quality improvement initiatives. Doctor of Nursing Practice students at a hospital-affiliated university participated in a pre-test, training, and post-test through an online learning management system. Training content and assessments focused on data and planning for its use in quality improvement initiatives. Sixteen students experienced a median of 17.6% increase in scores after completing the post-test. There was a statistically significant increase in scores between the pre-test and post-test (P = .0006). These results suggest educational content included in the Doctor of Nursing Practice Quality Improvement Toolkit increases knowledge about use and management of EHR data. Future considerations include use for educating a variety of students and healthcare staff.

Is Geographic Socioeconomic Disadvantage Associated with the Rate of THA in Medicare-aged Patients?

BACKGROUND

Disparities in THA use may lead to inequitable care. Prior research has focused on disparities based on individual-level and isolated socioeconomic and demographic variables. To our knowledge, the role of composite, community-level geographic socioeconomic disadvantage has not been studied in the United States. As disparities persist, exploring the potential underlying drivers of these inequities may help in developing more targeted recommendations on how to achieve equitable THA use.

QUESTIONS/PURPOSES

(1) Is geographic socioeconomic disadvantage associated with decreased THA rates in Medicare-aged patients? (2) Do these associations persist after adjusting for differences in gender, race, ethnicity, and proximity to hospitals performing THA?

METHODS

In a study with a cross-sectional design, using population-based data from five-digit ZIP codes in Maryland, USA, from July 1, 2012 to March 31, 2019, we included all inpatient and outpatient primary THAs performed in individuals 65 years of age or older at acute-care hospitals in Maryland, as reported in the Health Services Cost Review Commission database. This database was selected because it provided the five-digit ZIP code data necessary to answer our study question. We excluded THAs performed for nonelective indications. We examined the annual rate of THA in our study population for each Maryland ZIP code, adjusted for differences across areas in distributions of gender, race, ethnicity, and distance to the nearest hospital performing THAs. Four hundred fourteen ZIP codes were included, with an overall mean ± SD THA rate of 371 ± 243 per 100,000 persons 65 years or older, a rate similar to that previously reported in individuals aged 65 to 84 in the United States. Statistical significance was assessed at α = 0.05.

RESULTS

THA rates were higher in more affluent areas, with the following mean rates per 100,000 persons 65 years or older: 422 ± 259 in the least socioeconomically disadvantaged quartile, 339 ± 223 in the second-least disadvantaged, 277 ± 179 in the second-most disadvantaged, and 214 ± 179 in the most-disadvantaged quartile (p < 0.001). After adjustment for distributions in gender, race, ethnicity, and hospital proximity, we found that geographic socioeconomic disadvantage was still associated with THA rate. Compared with the least-disadvantaged quartile, the second-least disadvantaged quartile had 63 fewer THAs per 100,000 people (95% confidence interval 12 to 114), the second-most disadvantaged quartile had 136 fewer THAs (95% CI 62 to 211), and the most-disadvantaged quartile had 183 fewer THAs (95% CI 41 to 325).

CONCLUSION

Geographic socioeconomic disadvantage may be the underlying driver of disparities in THA use. Although our study does not determine the "correct" rate of THA, our findings support increasing access to elective orthopaedic surgery in disadvantaged geographic communities, compared with prior research and efforts that have studied and intervened on the basis of isolated factors such as race and gender. Increasing access to orthopaedic surgeons in disadvantaged neighborhoods, educating physicians about when surgical referral is appropriate, and educating patients from these geographic communities about the risks and benefits of THA may improve equitable orthopaedic care across neighborhoods. Future studies should explore disparities in rates of appropriate THA and the role of density of orthopaedic surgeons in an area.

LEVEL OF EVIDENCE

Level III, therapeutic study.

Utility of the Current Procedural Terminology Codes for Prophylactic Stabilization for Defining Metastatic Femur Disease

INTRODUCTION

Cohorts from the electronic health record are often defined by the Current Procedural Terminology (CPT) codes. The error prevalence of CPT codes for patients receiving surgical treatment of metastatic disease of the femur has not been investigated, and the predictive value of coding ontologies to identify patients with metastatic disease of the femur has not been adequately discussed.

METHODS

All surgical cases at a single academic tertiary institution from 2010 through 2015 involving prophylactic stabilization of the femur or fixation of a pathologic fracture of the femur were identified using the CPT and International Classification of Disease (ICD) codes. A detailed chart review was conducted to determine the procedure performed as documented in the surgical note and the patient diagnosis as documented in the pathology report, surgical note, and/or office visit notes.

RESULTS

We identified 7 CPT code errors of 171 prophylactic operations (4.1%) and one error of 71 pathologic fracture fixation s(1.4%). Of the 164 prophylactic operations that were coded correctly, 87 (53.0%) had metastatic disease. Of the 70 pathologic operations that were coded correctly, 41 (58%) had metastatic disease.

DISCUSSION

The error prevalence was low in both prophylactic stabilization and pathologic fixation groups (4.1% and 1%, respectively). The structured data (CPT and ICD-9 codes) had a positive predictive value for patients having metastatic disease of 53% for patients in the prophylactic stabilization group and 58% for patients in the pathologic fixation group. The CPT codes and ICD codes assessed in this analysis do provide a useful tool for defining a population in which a moderate proportion of individuals have metastatic disease in the femur at an academic medical center. However, verification is necessary.

Data extraction for epidemiological research (DExtER): a novel tool for automated clinical epidemiology studies

The use of primary care electronic health records for research is abundant. The benefits gained from utilising such records lies in their size, longitudinal data collection and data quality. However, the use of such data to undertake high quality epidemiological studies, can lead to significant challenges particularly in dealing with misclassification, variation in coding and the significant effort required to pre-process the data in a meaningful format for statistical analysis. In this paper, we describe a methodology to aid with the extraction and processing of such databases, delivered by a novel software programme; the "Data extraction for epidemiological research" (DExtER). The basis of DExtER relies on principles of extract, transform and load processes. The tool initially provides the ability for the healthcare dataset to be extracted, then transformed in a format whereby data is normalised, converted and reformatted. DExtER has a user interface designed to obtain data extracts specific to each research question and observational study design. There are facilities to input the requirements for; eligible study period, definition of exposed and unexposed groups, outcome measures and important baseline covariates. To date the tool has been utilised and validated in a multitude of settings. There have been over 35 peer-reviewed publications using the tool, and DExtER has been implemented as a validated public health surveillance tool for obtaining accurate statistics on epidemiology of key morbidities. Future direction of this work will be the application of the framework to linked as well as international datasets and the development of standardised methods for conducting electronic pre-processing and extraction from datasets for research purposes.

Time-driven activity-based costing for patients with multiple chronic conditions: a mixed-method study to cost care in a multidisciplinary and integrated care delivery centre at a university-affiliated tertiary teaching hospital in Stockholm, Sweden

OBJECTIVE

This study can be applied to cost the complex non-standardised processes used to treat patients with multiple chronic conditions.

DESIGN

A mixed-method approach to cost analysis, following a modified healthcare-specific version of the seven-step Time-Driven Activity-Based Costing (TDABC) approach.

SETTING

A multidisciplinary integrated and person-centred care delivery centre at a university-affiliated tertiary teaching hospital in Stockholm, Sweden, designed to improve care coordination for patients with multiple chronic conditions, specifically diabetes, cardiovascular disease and kidney disease.

PARTICIPANTS

314 patients (248 men and 66 women) fit inclusion criteria. Average age was 80 years.

RESULTS

This modified TDABC analysis costed outpatient care for patients with multiple chronic conditions. The approach accounted for the difficulty of conceptualising care cycles. The estimated total cost, stratified by resources, can be reviewed together with existing managerial accounting statements to inform management decisions regarding the multidisciplinary centre.

CONCLUSIONS

This article demonstrates that the healthcare-specific seven-step approach to TDABC can be applied to cost care for patients with multiple chronic conditions, where pathways are not yet discernable. It became clear that there was a need for slight methodological adaptations for this particular patient group to make it possible to cost these pathways, stratified by activity and resource. The value of this approach can be discerned from the way management incorporated the results of this analysis into the development of their hospital strategy. In the absence of integrated data infrastructures that can link patients and resources across financial, clinical and process data sets, the scalability of this method will be difficult.

Comparison of Measures to Predict Mortality and Length of Stay in Hospitalized Patients

BACKGROUND

Patient risk adjustment is critical for hospital benchmarking and allocation of healthcare resources. However, considerable heterogeneity exists among measures.

OBJECTIVES

The performance of five measures was compared to predict mortality and length of stay (LOS) in hospitalized adults using claims data; these include three comorbidity composite scores (Charlson/Deyo age-comorbidity score, V W Elixhauser comorbidity score, and V W Elixhauser age-comorbidity score), 3 M risk of mortality (3 M ROM), and 3 M severity of illness (3 M SOI) subclasses.

METHODS

Binary logistic and zero-truncated negative binomial regression models were applied to a 2-year retrospective dataset (2013-2014) with 123,641 adult inpatient admissions from a large hospital system in New York City.

RESULTS

All five measures demonstrated good to strong model fit for predicting in-hospital mortality, with C-statistics of 0.74 (95% confidence interval [CI] [0.74, 0.75]), 0.80 (95% CI [0.80, 0.81]), 0.81(95% CI [0.81, 0.82]), 0.94 (95% CI [0.93, 0.94]), and 0.90 (95% CI [0.90, 0.91]) for Charlson/Deyo age-comorbidity score, V W Elixhauser comorbidity score, V W Elixhauser age-comorbidity score, 3 M ROM, and 3 M SOI, respectively. The model fit statistics to predict hospital LOS measured by the likelihood ratio index were 0.3%, 1.2%, 1.1%, 6.2%, and 4.3%, respectively.

DISCUSSION

The measures tested in this study can guide nurse managers in the assignment of nursing care and coordination of needed patient services and administrators to effectively and efficiently support optimal nursing care.

Strategies for overcoming language barriers in research

AIM

This paper seeks to describe best practices for conducting cross-language research with individuals who have a language barrier.

DESIGN

Discussion paper.

DATA SOURCES

Research methods papers addressing cross-language research issues published between 2000-2017.

IMPLICATIONS FOR NURSING

Rigorous cross-language research involves the appropriate use of interpreters during the research process, systematic planning for how to address the language barrier between participant and researcher and the use of reliably and validly translated survey instruments (when applicable). Biases rooted in those who enter data into "big data" systems may influence data quality and analytic approaches in large observational studies focused on linking patient language preference to health outcomes.

CONCLUSION

Cross-language research methods can help ensure that those individuals with language barriers have their voices contributing to the evidence informing healthcare practice and policies that shape health services implementation and financing. Understanding the inherent conscious and unconscious biases of those conducting research with this population and how this may emerge in research studies is also an important part of producing rigorous, reliable, and valid cross-language research.

IMPACT

This study synthesized methodological recommendations for cross-language research studies with the goal to improve the quality of future research and expand the evidence-base for clinical practice. Clear methodological recommendations were generated that can improve research rigor and quality of cross-language qualitative and quantitative studies. The recommendations generated here have the potential to have an impact on the health and well-being of migrants around the world.

Experience and reflection from China's Xiangya medical big data project

The construction of medical big data includes several problems that need to be solved, such as integration and data sharing of many heterogeneous information systems, efficient processing and analysis of large-scale medical data with complex structure or low degree of structure, and narrow application range of medical data. Therefore, medical big data construction is not only a simple collection and application of medical data but also a complex systematic project. This paper introduces China's experience in the construction of a regional medical big data ecosystem, including the overall goal of the project; establishment of policies to encourage data sharing; handling the relationship between personal privacy, information security, and information availability; establishing a cooperation mechanism between agencies; designing a polycentric medical data acquisition system; and establishing a large data centre. From the experience gained from one of China's earliest established medical big data projects, we outline the challenges encountered during its development and recommend approaches to overcome these challenges to design medical big data projects in China more rationally. Clear and complete top-level design of a project requires to be planned in advance and considered carefully. It is essential to provide a culture of information sharing and to facilitate the opening of data, and changes in ideas and policies need the guidance of the government. The contradiction between data sharing and data security must be handled carefully, that is not to say data openness could be abandoned. The construction of medical big data involves many institutions, and high-level management and cooperation can significantly improve efficiency and promote innovation. Compared with infrastructure construction, it is more challenging and time-consuming to develop appropriate data standards, data integration tools and data mining tools.

Incidence and physiological mechanism of carboplatin-induced electrolyte abnormality among patients with non-small cell lung cancer

To clarify the association between carboplatin and electrolyte abnormality, a pooled-analysis was performed with the adverse event reports of non-small cell lung cancer patients. A total of 19901 adverse events were retrieved from the FDA Adverse Event Reporting System (FAERS). Pooled reporting odds ratios (RORs) and 95% CIs suggested that carboplatin was significantly associated with hyponatremia (pooled ROR = 1.57, 95% CI 1.18-2.09, P = 1.99×10-3) and hypokalemia (pooled ROR = 2.37, 95% CI 1.80-3.10, P = 5.24×10-10) as compared to other therapies. In addition, we found that dehydration was frequently concurrent with carboplatin therapy (pooled ROR = 2.01, 95% CI 1.52-2.66, P = 8.37×10-7), which may prompt excessive water ingestion and decrease serum electrolyte concentrations. This information has not been mentioned in the FDA-approved drug label and could help explain the physiological mechanism of carboplatin-induced electrolyte abnormality. In conclusion, the above results will facilitate clinical management and prompt intervention of life-threatening electrolyte imbalance in the course of cancer treatment.

Applying STOPP Guidelines in Primary Care Through Electronic Medical Record Decision Support: Randomized Control Trial Highlighting the Importance of Data Quality

Background

Potentially Inappropriate Prescriptions (PIPs) are a common cause of morbidity, particularly in the elderly.

Objective

We sought to understand how the Screening Tool of Older People’s Prescriptions (STOPP) prescribing criteria, implemented in a routinely used primary care Electronic Medical Record (EMR), could impact PIP rates in community (non-academic) primary care practices.

Methods

We conducted a mixed-method, pragmatic, cluster, randomized control trial in research naïve primary care practices. Phase 1: In the randomized controlled trial, 40 fully automated STOPP rules were implemented as EMR alerts during a 16-week intervention period. The control group did not receive the 40 STOPP rules (but received other alerts). Participants were recruited through the OSCAR EMR user group mailing list and in person at user group meetings. Results were assessed by querying EMR data PIPs. EMR data quality probes were included. Phase 2: physicians were invited to participate in 1-hour semi-structured interviews to discuss the results.

Results

In the EMR, 40 STOPP rules were successfully implemented. Phase 1: A total of 28 physicians from 8 practices were recruited (16 in intervention and 12 in control groups). The calculated PIP rate was 2.6% (138/5308) (control) and 4.11% (768/18,668) (intervention) at baseline. No change in PIPs was observed through the intervention (P=.80). Data quality probes generally showed low use of problem list and medication list. Phase 2: A total of 5 physicians participated. All the participants felt that they were aware of the alerts but commented on workflow and presentation challenges.

Conclusions

The calculated PIP rate was markedly less than the expected rate found in literature (2.6% and 4.0% vs 20% in literature). Data quality probes highlighted issues related to completeness of data in areas of the EMR used for PIP reporting and by the decision support such as problem and medication lists. Users also highlighted areas for better integration of STOPP guidelines with prescribing workflows. Many of the STOPP criteria can be implemented in EMRs using simple logic. However, data quality in EMRs continues to be a challenge and was a limiting step in the effectiveness of the decision support in this study. This is important as decision makers continue to fund implementation and adoption of EMRs with the expectation of the use of advanced tools (such as decision support) without ongoing review of data quality and improvement.

Trial Registration

Clinicaltrials.gov NCT02130895; https://clinicaltrials.gov/ct2/show/NCT02130895 (Archived by WebCite at http://www.webcitation.org/6qyFigSYT)

Electronic Surveillance of Surgical Site Infections

BACKGROUND

Electronic health and administrative data are increasingly being used for identifying surgical site infections (SSI). We found an unexpectedly high number of patients who could not be classified definitively as having an infection or not. To further explore this, we present an electronic classification algorithm for conservative case finding and identify alterations that would adapt the method for other purposes.

METHODS

Two computer algorithms were created to identify SSI. One model used a strict National Healthcare Safety Network (NHSN) based SSI algorithm, which was applied to all discharges from 443,284 all discharges from four hospitals in Manhattan, NY, 2009 through 2012. The second model used discharges that only had NHSN-defined SSI procedures during the same period.

RESULTS

The strict SSI algorithm was able to classify SSI status for 27.3% of discharges; there was a high number of indeterminate cases. In contrast, the modified, less strict model, classified 97.2% of discharges with NHSN-approved SSI procedures.

CONCLUSION

Electronic records provide several options for aiding with the identification of infections in healthcare settings and can be tailored to suit specific uses. While algorithms for SSI classification should reflect the NHSN definition, our research emphasizes how variations of model building can affect the number of indeterminate cases that may necessitate manual review.

Impact of nursing diagnoses on patient and organisational outcomes: a systematic literature review

AIMS AND OBJECTIVES

To investigate the impact of nursing diagnoses on patient and organisational outcomes in any field of health care where nurses are involved.

BACKGROUND

In healthcare systems, descriptions of patient complexity and outcomes and payment criteria are primarily based on medical diagnoses and procedures. Other aspects of patient care are rarely considered. Nursing diagnoses are believed to be related to healthcare outcomes, but comprehensive evidence for this association is missing.

DESIGN

Systematic literature review.

METHODS

The search was conducted in PubMed, CINAHL and Scopus databases without year or language limitations. The studies were categorised according to their methodological quality (low, good or high) and classified based on their levels of evidence on a scale of 1 (strongest evidence) to 5 (weakest evidence).

RESULTS

Seventeen of 3426 potentially relevant studies met the eligibility criteria. Eleven studies were classified as low, five as good and one as high quality. The levels of evidence were rated as 2 for one study, three for two studies, four for nine studies and five for five studies. Nursing diagnoses were found to predict patient (quality of life, mortality) and organisational (length of hospital stay, hospital charges, amount of nursing care, discharge dispositions) outcomes. Patient care plans based on nursing diagnoses improved sleep quality, quality of life and glycaemic control. When added to information from disease-based classification systems (e.g. diagnosis-related groups), nursing diagnoses improved the predictions of the above outcomes.

CONCLUSIONS

Nursing diagnoses have a great potential to predict patient and organisational outcomes. High-quality research is required to better investigate the existence and strength of these relationships.

RELEVANCE TO CLINICAL PRACTICE

The systematic use of nursing diagnoses in clinical practice, as well as the sharing of high-quality nursing data in large databases, may provide a considerable boost to the contribution of nursing to healthcare outcomes.

Utilization of Large Data Sets in Maternal Health in Finland: A Case for Global Health Research

In recent years, the use of large data sets, such as electronic health records, has increased. These large data sets are often referred to as "Big Data," which have various definitions. The purpose of this article was to summarize and review the utilization, strengths, and challenges of register data, which means a written record containing regular entries of items or details, and Big Data, especially in maternal nursing, using 4 examples of studies from the Finnish Medical Birth Register data and relate these to other international databases and data sets. Using large health register data is crucial when studying and understanding outcomes of maternity care. This type of data enables comparisons on a population level and can be utilized in research related to maternal health, with important issues and implications for future research and clinical practice. Although there are challenges connected with register data and Big Data, these large data sets offer the opportunity for timely insight into population-based information on relevant research topics in maternal health. Nurse researchers need to understand the possibilities and limitations of using existing register data in maternity research. Maternal child nurse researchers can be leaders of the movement to utilize Big Data to improve global maternal health.

The Need for a Definition of Big Data for Nursing Science: A Case Study of Disaster Preparedness

The rapid development of technology has made enormous volumes of data available and achievable anytime and anywhere around the world. Data scientists call this change a data era and have introduced the term "Big Data", which has drawn the attention of nursing scholars. Nevertheless, the concept of Big Data is quite fuzzy and there is no agreement on its definition among researchers of different disciplines. Without a clear consensus on this issue, nursing scholars who are relatively new to the concept may consider Big Data to be merely a dataset of a bigger size. Having a suitable definition for nurse researchers in their context of research and practice is essential for the advancement of nursing research. In view of the need for a better understanding on what Big Data is, the aim in this paper is to explore and discuss the concept. Furthermore, an example of a Big Data research study on disaster nursing preparedness involving six million patient records is used for discussion. The example demonstrates that a Big Data analysis can be conducted from many more perspectives than would be possible in traditional sampling, and is superior to traditional sampling. Experience gained from the process of using Big Data in this study will shed light on future opportunities for conducting evidence-based nursing research to achieve competence in disaster nursing.

Challenges in patient safety improvement research in the era of electronic health records

Electronic health record (EHR) data repositories contain large volumes of aggregated, longitudinal clinical data that could allow patient safety researchers to identify important safety issues and conduct comprehensive evaluations of health care delivery outcomes. However, few health systems have successfully converted this abundance of data into useful information or knowledge for safety improvement. In this paper, we use a case study involving a project on missed/delayed follow-up of test results to discuss real-world challenges in using EHR data for patient safety research. We identify three types of challenges that pose as barriers to advance patient safety improvement research: 1) gaining approval to access/review EHR data; 2) interpreting EHR data; 3) working with local IT/EHR personnel. We discuss the complexity of these challenges, all of which are unlikely to be unique to this project, and outline some key next steps that must be taken to support research that uses EHR data to improve safety. We recognize that all organizations face competing priorities between clinical operations and research. However, to leverage EHRs and their abundant data for patient safety improvement research, many current data access and security policies and procedures must be rewritten and standardized across health care organizations. These efforts are essential to help make EHRs and EHR data useful for progress in our journey to safer health care.

Informatics competencies essential to decision making in nursing management

Abstract OBJECTIVE To identify informatics abilities essential to decision making in nursing management. METHOD Survey study with specialist nurses in health informatics and management. An electronic questionnaire was built based on the competencies Information Literacy (five categories; 40 abilities) and Information Management (nine categories; 69 abilities) of the TIGER - Technology Informatics Guiding Education Reform - initiative, with the guiding question: Which informatics abilities are essential to decision making in management? Answers were sorted in a Likert scale, ranging from 1 to 5. Rasch analysis was conducted with the software WINSTEPS ®. Results were presented in logits, with cutoff value zero. RESULTS Thirty-two specialists participated, coming from all regions of Brazil. In the information literacy competency, 18 abilities were considered essential and in Information Management, 38; these were sorted according to their degree of essentiality. CONCLUSION It is believed that the incorporation of these abilities in teaching can support the education of nurse managers and contribute to evidence-based practice, incorporation of information and communication technologies in health and information management.