The strategic management of data quality in healthcare

Qualitative study of UK health and care professionals to determine resources and processes that can support actions to improve quality of data used to address and monitor health inequalities

INTRODUCTION

Health inequalities in the UK are investigated and addressed by analysing data across socioeconomic factors, geography and specific characteristics, including those protected under law. It is acknowledged that the quality of data underpinning these analyses can be improved. The objective of this work was to gain insights from professionals working across the health and care sector in England into the type(s) of resource(s) that can be instrumental in implementing mechanisms to improve data quality into practice.

DESIGN

Qualitative study based on semistructured interviews involving health and care professionals.

SETTING

England.

PARTICIPANTS

A total of 16 professionals, mainly from the East of England.

RESULTS

Awareness of mechanisms that could be put in place to improve quality of data related to health inequalities was high among interviewees. However, logistical (eg, workforce time, capacity and funding) as well as data usage (eg, differences in data granularity, information governance structures) barriers impacted on implementation of many mechanisms. Participants also acknowledged that concepts and priorities around health inequalities can vary across the system. While there are resources already available that can aid in improving data quality, finding them and ensuring they are suited to needs was time-consuming. Our analysis indicates that resources to support the creation of a shared understanding of what health inequalities are and share knowledge of specific initiatives to improve data quality between systems, organisations and individuals are useful.

CONCLUSIONS

Different resources are needed to support actions to improve quality of data used to investigate heath inequalities. These include those aimed at raising awareness about mechanisms to improve data quality as well as those addressing system-level issues that impact on implementation. The findings of this work provide insights into actionable steps local health and care services can take to improve the quality of data used to address health inequalities.

Variational Autoencoders for Biomedical Signal Morphology Clustering and Noise Detection

Accurate estimation of physiological biomarkers using raw waveform data from non-invasive wearable devices requires extensive data preprocessing. An automatic noise detection method in time-series data would offer significant utility for various domains. As data labeling is onerous, having a minimally supervised abnormality detection method for input data, as well as an estimation of the severity of the signal corruptness, is essential. We propose a model-free, time-series biomedical waveform noise detection framework using a Variational Autoencoder coupled with Gaussian Mixture Models, which can detect a range of waveform abnormalities without annotation, providing a confidence metric for each segment. Our technique operates on biomedical signals that exhibit periodicity of heart activities. This framework can be applied to any machine learning or deep learning model as an initial signal validator component. Moreover, the confidence score generated by the proposed framework can be incorporated into different models' optimization to construct confidence-aware modeling. We conduct experiments using dynamic time warping (DTW) distance of segments to validated cardiac cycle morphology. The result confirms that our approach removes noisy cardiac cycles and the remaining signals, classified as clean, exhibit a 59.92% reduction in the standard deviation of DTW distances. Using a dataset of bio-impedance data of 97885 cardiac cycles, we further demonstrate a significant improvement in the downstream task of cuffless blood pressure estimation, with an average reduction of 2.67 mmHg root mean square error (RMSE) of Diastolic Blood pressure and 2.13 mmHg RMSE of systolic blood pressure, with increases of average Pearson correlation of 0.28 and 0.08, with a statistically significant improvement of signal-to-noise ratio respectively in the presence of different synthetic noise sources. This enables burden-free validation of wearable sensor data for downstream biomedical applications.

What Does Information Science Offer for Data Science Research?: A Review of Data and Information Ethics Literature

This paper reviews literature pertaining to the development of data science as a discipline, current issues with data bias and ethics, and the role that the discipline of information science may play in addressing these concerns. Information science research and researchers have much to offer for data science, owing to their background as transdisciplinary scholars who apply human-centered and social-behavioral perspectives to issues within natural science disciplines. Information science researchers have already contributed to a humanistic approach to data ethics within the literature and an emphasis on data science within information schools all but ensures that this literature will continue to grow in coming decades. This review article serves as a reference for the history, current progress, and potential future directions of data ethics research within the corpus of information science literature.

Developing a systematic approach to assessing data quality in secondary use of clinical data based on intended use

INTRODUCTION

Secondary use of electronic health record (EHR) data for research requires that the data are fit for use. Data quality (DQ) frameworks have traditionally focused on structural conformance and completeness of clinical data extracted from source systems. In this paper, we propose a framework for evaluating semantic DQ that will allow researchers to evaluate fitness for use prior to analyses.

METHODS

We reviewed current DQ literature, as well as experience from recent multisite network studies, and identified gaps in the literature and current practice. Derived principles were used to construct the conceptual framework with attention to both analytic fitness and informatics practice.

RESULTS

We developed a systematic framework that guides researchers in assessing whether a data source is fit for use for their intended study or project. It combines tools for evaluating clinical context with DQ principles, as well as factoring in the characteristics of the data source, in order to develop semantic DQ checks.

CONCLUSIONS

Our framework provides a systematic process for DQ development. Further work is needed to codify practices and metadata around both structural and semantic data quality.

Conversion to an electronic missing medication request system at an academic medical center

Missing medications can negatively contribute to the financial and operational workflows of pharmacy departments and add medication safety challenges. The missing medication request (MMR) system at the study institution converted to entirely electronic in June 2018 from a hybrid electronic system. This study evaluated 4-week periods pre- and post-conversion. The objective of this study was to evaluate the impact of conversion to an electronic MMR system on the quantity of requests received at an academic medical center. The average daily number of MMR's decreased from the pre-conversion group to the post-conversion group (1.77 (±0.16) vs 1.48 (±0.17), p < 0.001). During post-conversion, the median triage time was 8 min [3 min-19 min], pharmacists triaged 62.4% of requests, and 29.6% of requests were declined. Conversion to an electronic MMR system represents one solution to decreasing missing medications. Future studies are needed to evaluate the financial, operational, and medication safety impact of conversion.

Quality of patient information in emergency department

PURPOSE

Data comprise one of the key resources currently used in organizations. High-quality data are those that are appropriate for use by the customer. The quality of data is a key factor in determining the level of healthcare in hospitals, and its improvement leads to an improved quality of health and treatment and ultimately increases patient satisfaction. The purpose of this paper is to assess the quality of emergency patients' information in a hospital information system.

DESIGN/METHODOLOGY/APPROACH

This cross-sectional study was conducted on 385 randomly selected records of patients admitted to the emergency department of Shahid Beheshti Hospital in Kashan, Iran, in 2016. Data on five dimensions of quality, including accuracy, accessibility, timeliness, completeness and definition, were collected using a researcher-made checklist and were then analyzed in SPSS. The results are presented using descriptive statistics, such as frequency distribution and percentage.

FINDINGS

The overall quality of emergency patients' information in the hospital information system was 86 percent, and the dimensions of quality scored 87.7 percent for accuracy, 86.8 percent for completeness, 83.9 percent for timeliness, 79 percent for definition and 62.1 percent for accessibility.

ORIGINALITY/VALUE

Increasing the quality of patient information at emergency departments can lead to improvements in the timely diagnosis and management of diseases and patient and personnel satisfaction, and reduce hospital costs.

Exploring completeness in clinical data research networks with DQe-c

Objective

To provide an open source, interoperable, and scalable data quality assessment tool for evaluation and visualization of completeness and conformance in electronic health record (EHR) data repositories.

Materials and Methods

This article describes the tool’s design and architecture and gives an overview of its outputs using a sample dataset of 200 000 randomly selected patient records with an encounter since January 1, 2010, extracted from the Research Patient Data Registry (RPDR) at Partners HealthCare. All the code and instructions to run the tool and interpret its results are provided in the Supplementary Appendix.

Results

DQ^e-c produces a web-based report that summarizes data completeness and conformance in a given EHR data repository through descriptive graphics and tables. Results from running the tool on the sample RPDR data are organized into 4 sections: load and test details, completeness test, data model conformance test, and test of missingness in key clinical indicators.

Discussion

Open science, interoperability across major clinical informatics platforms, and scalability to large databases are key design considerations for DQ^e-c. Iterative implementation of the tool across different institutions directed us to improve the scalability and interoperability of the tool and find ways to facilitate local setup.

Conclusion

EHR data quality assessment has been hampered by implementation of ad hoc processes. The architecture and implementation of DQ^e-c offer valuable insights for developing reproducible and scalable data science tools to assess, manage, and process data in clinical data repositories.

Data Quality: A Negotiator between Paper-Based and Digital Records in Pakistan’s TB Control Program

Background: The cornerstone of the public health function is to identify healthcare needs, to influence policy development, and to inform change in practice. Current data management practices with paper-based recording systems are prone to data quality defects. Increasingly, healthcare organizations are using technology for the efficient management of data. The aim of this study was to compare the data quality of digital records with the quality of the corresponding paper-based records using a data quality assessment framework. Methodology: We conducted a desk review of paper-based and digital records over the study duration from April 2016 to July 2016 at six enrolled tuberculosis (TB) clinics. We input all data fields of the patient treatment (TB01) card into a spreadsheet-based template to undertake a field-to-field comparison of the shared fields between TB01 and digital data. Findings: A total of 117 TB01 cards were prepared at six enrolled sites, whereas just 50% of the records (n = 59; 59 out of 117 TB01 cards) were digitized. There were 1239 comparable data fields, out of which 65% (n = 803) were correctly matched between paper based and digital records. However, 35% of the data fields (n = 436) had anomalies, either in paper-based records or in digital records. The calculated number of data quality issues per digital patient record was 1.9, whereas it was 2.1 issues per record for paper-based records. Based on the analysis of valid data quality issues, it was found that there were more data quality issues in paper-based records (n = 123) than in digital records (n = 110). Conclusion: There were fewer data quality issues in digital records as compared with the corresponding paper-based records of tuberculosis patients. Greater use of mobile data capture and continued data quality assessment can deliver more meaningful information for decision making.

Information Quality Awareness and Information Quality Practice

Healthcare organizations increasingly rely on electronic information to optimize their operations. Information of high diversity from various sources accentuate the relevance and importance of information quality (IQ). The quality of information needs to be improved to support a more efficient and reliable utilization of healthcare information systems (IS). This can only be achieved through the implementation of initiatives followed by most users across an organization. The purpose of this study is to examine how awareness of IS users about IQ issues would affect their IQ behavior. Based on multiple theoretical frameworks, it is hypothesized that different aspects of user motivation mediate the relationship between the awareness on both beneficial and problematic situations and IQ practice inclination. In addition, social influence and facilitating condition moderate the relationship between IQ practice inclination and overt IQ practice. The theoretical and practical implications of findings are discussed, especially how to enhance IQ compliance in the healthcare settings.

Developing a tool to assess the quality of socio-demographic data in community health centres

OBJECTIVE

The objectives of this study are to 1) create a quality assessment tool for socio-demographic data aligned with the needs of Community Health Centres (CHCs) and based on the data quality framework of the Canadian Institute for Health Information (CIHI), and 2) test the feasibility of the tool in CHCs.

METHODS

The tool was developed based on both theoretical and practical knowledge. A review of the literature was performed to identify data quality frameworks and dimensions that could be employed. In addition, informal discussions with Community Health Centres staff members holding various positions were conducted and a team of subject matter experts was established. This approach supported the alignment between the tool (i.e., the indicators developed, the rating scale, and weighting system) and the setting for which it has been designed. The tool was pilot tested in five CHCs across Ontario.

RESULTS

The decision to focus on socio-demographic data was based on findings from the discussions with staff members. The team established nine principles for the development of the tool, including the use of computer software, whenever possible, to query the data and ensure consistency of the measurement. Data quality scores ranged from 45 to 74 on a scale of 0 (lowest quality) to 100 (highest data quality), with one CHC that was not able to run all of the queries. The feedback from staff was positive and supports the feasibility of the tool as an application of the CIHI data quality framework in a local setting.

CONCLUSION

Pilot test results demonstrate the feasibility of the tool and an applicability of the CIHI framework as a basis for developing tools for data quality assessment in health care organizations.

Data quality assessment in healthcare: a 365-day chart review of inpatients' health records at a Nigerian tertiary hospital

BACKGROUND

Health records are essential for good health care. Their quality depends on accurate and prompt documentation of the care provided and regular analysis of content. This study assessed the quantitative properties of inpatient health records at the Federal Medical Centre, Bida, Nigeria.

METHOD

A retrospective study was carried out to assess the documentation of 780 paper-based health records of inpatients discharged in 2009.

RESULTS

732 patient records were reviewed from the departments of obstetrics (45.90%), pediatrics (24.32%), and other specialties (29.78%). Documentation performance was very good (98.49%) for promptness recording care within the first 24 h of admission, fair (58.80%) for proper entry of patient unit number (unique identifier), and very poor (12.84%) for utilization of discharge summary forms. Overall, surgery records were nearly always (100%) prompt regarding care documentation, obstetrics records were consistent (80.65%) in entering patients' names in notes, and the principal diagnosis was properly documented in all (100%) completed discharge summary forms in medicine. 454 (62.02%) folders were chronologically arranged, 456 (62.29%) were properly held together with file tags, and most (80.60%) discharged folders reviewed, analyzed and appropriate code numbers were assigned.

CONCLUSIONS

Inadequacies were found in clinical documentation, especially gross underutilization of discharge summary forms. However, some forms were properly documented, suggesting that hospital healthcare providers possess the necessary skills for quality clinical documentation but lack the will. There is a need to institute a clinical documentation improvement program and promote quality clinical documentation among staff.

Semantic interoperability in standardized electronic health record databases

Different clinics and hospitals have their own information systems to maintain patient data. This hinders the exchange of data among systems (and organizations). Hence there is a need to provide standards for data exchange. In digitized form, the individual patient's medical record can be stored, retrieved, and shared over a network through enhancement in information technology. Thus, electronic health records (EHRs) should be standardized, incorporating semantic interoperability. A subsequent step requires that healthcare professionals and patients get involved in using the EHRs, with the help of technological developments. This study aims to provide different approaches in understanding some current and challenging concepts in health informatics. Successful handling of these challenges will lead to improved quality in healthcare by reducing medical errors, decreasing costs, and enhancing patient care. The study is focused on the following goals: (1) understanding the role of EHRs; (2) understanding the need for standardization to improve quality; (3) establishing interoperability in maintaining EHRs; (4) examining a framework for standardization and interoperability (the openEHR architecture; (5) identifying the role of archetypes for knowledge-based systems; and (6) understanding the difficulties in querying HER data.

Looking ahead: clinical trial design in adult congenital heart disease

Continued growth in the field of adult congenital heart disease has given rise to a critical mass of patients, investigators and resources dedicated to advancing knowledge through research. Impressive gains over the past decade have largely been driven by successful observational studies. Despite the privileged role of clinical trials in the hierarchy of evidence-based medicine, few such studies have thus far been conducted in adult congenital heart disease. Obstacles and challenges particular to clinical trials in adult congenital heart disease are addressed in this review. In looking ahead, examples of creative methodological solutions to maximizing the efficiency of clinical trials for rare diseases are discussed, including Bayesian analyses, outcome-adaptive randomization and internal pilot studies.

Good health information - an asset not a burden!

Good health information is central to informing the delivery of health care. Health has mostly struggled to promote the effective use of information to manage services on a day to day basis. Based on the experience at the Children’s Hospital at Westmead, a case is made for seeing information as an asset that requires a structured approach to improving data quality, and making a concerted effort to grow a more robust information culture. Transforming Health through better health information will not happen overnight. It needs a long range plan. It should be supported by appropriate business intelligence tools and a structured approach to process improvement, built around data management. What is known about the topic? Good Health Information is central to informing the delivery of health care. Health has mostly struggled to promote the effective use of information to manage services on a day to day basis. What does this paper add? A case is made for seeing information as an asset that requires a structured approach to improving data quality and a concerted effort to grow a more robust information culture. What are the implications for practitioners? Health is at a point where far greater use can be made of available information assets and the development of staff. Good health information needs to be seen as an asset. Health facilities need to recognise the importance of a clear vision for information management and how it can support the overall transformation of Health.