Improving Data Quality in Health Care

2010 ◽  
pp. 1877-1881
Author(s):  
Karolyn Kerr ◽  
Tony Norris
Keyword(s):  
Health Care ◽  
New Zealand ◽  
Data Quality ◽  
Good Practice ◽  
Health Data ◽  
Strategic Approach ◽  
The Right ◽  
Data Collections ◽  
And Control ◽  
Care Demands

The increasingly information intensive nature of health care demands a proactive and strategic approach to data quality to ensure the right information is available to the right person at the right time in the right format. The approach must also encompass the rights of the patient to have their health data protected and used in an ethical way. This article describes the principles to establish good practice and overcome practical barriers that define and control data quality in health data collections and the mechanisms and frameworks that can be developed to achieve and sustain quality. The experience of a national health data quality project in New Zealand is used to illustrate the issues.

Improving Data Quality in Health Care

2011 ◽  
pp. 218-225 ◽  
Author(s):  
Karolyn Kerr ◽  
Tony Norris
Keyword(s):  
Health Care ◽  
New Zealand ◽  
Data Quality ◽  
Good Practice ◽  
Health Data ◽  
Strategic Approach ◽  
The Right ◽  
Data Collections ◽  
And Control ◽  
Care Demands

The increasingly information intensive nature of health care demands a proactive and strategic approach to data quality to ensure the right information is available to the right person at the right time in the right format. The approach must also encompass the rights of the patient to have their health data protected and used in an ethical way. This article describes the principles to establish good practice and overcome practical barriers that define and control data quality in health data collections and the mechanisms and frameworks that can be developed to achieve and sustain quality. The experience of a national health data quality project in New Zealand is used to illustrate the issues.

Australian Health Review call for papers

2007 ◽  
Vol 31 (1) ◽  
pp. 7
Author(s):  
Sandra G Leggat
Keyword(s):  
Health Care ◽  
New Zealand ◽  
Information Management ◽  
Information And Communication Technologies ◽  
Care Delivery ◽  
Communication Technologies ◽  
Australian Health ◽  
Information And Communication ◽  
The Right ◽  
Health Review

Technology in health care: are we delivering on the promise? Australian Health Review invites contributions for an upcoming issue on information management and information and communication technology in health care. Submission deadline: 15 May 2007 Despite a reputation for less spending on information and communication technologies (ICT), the health care sector has an imperative to ensure the ?right? information has been made available and accessible to the ?right? person at the ?right? time. While there is increasing evidence that the strategic application of ICT in innovative ways can improve the effectiveness of health care delivery, we don?t often discuss the substantial changes to the way health care organisations operate that are required for best practice information management. In an upcoming issue, Australian Health Review is looking to publish feature articles, research papers, case studies and commentaries related to information management and information and communication technologies in health care. We are particularly interested in papers that report on the successes, or failures, of initiatives in Australia and New Zealand that have brought together the research, the technology and the clinical, managerial and organisational expertise. Submissions related to international initiatives with lessons for Australia and New Zealand will also be welcomed. Submissions can be short commentaries of 1000 to 2000 words, or more comprehensive reviews of 2000 to 4000 words. Please consult the AHR Guidelines for Authors for information on formatting and submission. The deadline for submission is 15 May 2007.

scholarly journals Clinicians and Older Adults’ Perceptions of the Utility of Patient-Generated Health Data in Caring for Older Adults: Exploratory Mixed Methods Study

JMIR Aging ◽  
10.2196/29788 ◽  
2021 ◽  
Vol 4 (4) ◽  
pp. e29788
Author(s):  
Ben Kim ◽  
Peyman Ghasemi ◽  
Paul Stolee ◽  
Joon Lee
Keyword(s):  
Older Adults ◽  
Health Care ◽  
Mixed Methods ◽  
Focus Group ◽  
Data Quality ◽  
Health Data ◽  
Perceived Usefulness ◽  
Mixed Methods Study ◽  
Focus Group Interviews ◽  
Group Interviews

Background Many people are motivated to self-track their health and optimize their well-being through mobile health apps and wearable devices. The diversity and complexity of these systems have evolved over time, resulting in a large amount of data referred to as patient-generated health data (PGHD), which has recently emerged as a useful set of data elements in health care systems around the world. Despite the increased interest in PGHD, clinicians and older adults’ perceptions of PGHD are poorly understood. In particular, although some clinician barriers to using PGHD have been identified, such as concerns about data quality, ease of use, reliability, privacy, and regulatory issues, little is known from the perspectives of older adults. Objective This study aims to explore the similarities and differences in the perceptions of older adults and clinicians with regard to how various types of PGHD can be used to care for older adults. Methods A mixed methods study was conducted to explore clinicians and older adults’ perceptions of PGHD. Focus groups were conducted with older adults and health care providers from the Greater Toronto area and the Kitchener-Waterloo region. The participants were asked to discuss their perceptions of PGHD, including facilitators and barriers. A questionnaire aimed at exploring the perceived usefulness of a range of different PGHD was also embedded in the study design. Focus group interviews were transcribed for thematic analysis, whereas the questionnaire results were analyzed using descriptive statistics. Results Of the 9 participants, 4 (44%) were clinicians (average age 38.3 years, SD 7 years), and 5 (56%) were older adults (average age 81.0 years, SD 9.1 years). Four main themes were identified from the focus group interviews: influence of PGHD on patient-provider trust, reliability of PGHD, meaningful use of PGHD and PGHD-based decision support systems, and perceived clinical benefits and intrusiveness of PGHD. The questionnaire results were significantly correlated with the frequency of PGHD mentioned in the focus group interviews (r=0.42; P=.03) and demonstrated that older adults and clinicians perceived blood glucose, step count, physical activity, sleep, blood pressure, and stress level as the most useful data for managing health and delivering high-quality care. Conclusions This embedded mixed methods study generated several important findings about older adults and clinicians’ perceptions and perceived usefulness of a range of PGHD. Owing to the exploratory nature of this study, further research is needed to understand the concerns about data privacy, potential negative impact on the trust between older adults and clinicians, data quality and quantity, and usability of PGHD-related technologies for older adults.

scholarly journals Opening government health data to the public: benefits, challenges, and lessons learned from early innovators

2016 ◽  
Vol 24 (2) ◽  
pp. 345-351 ◽  
Author(s):  
Erika G Martin ◽  
Grace M Begany
Keyword(s):  
Health Care ◽  
Data Quality ◽  
New York State ◽  
Open Data ◽  
Health Data ◽  
Community Empowerment ◽  
Theory Approach ◽  
Data Movement ◽  
Department Of Health ◽  
Open Data Movement

Objective: Government agencies are rapidly developing web portals to proactively publish “open” data that are searchable, available in nonproprietary formats, and with unlimited use and distribution rights. In this dynamic environment, we aimed to understand the experiences of 2 early leaders in open health data, the US Department of Health and Human Services and the New York State Department of Health. Materials and Methods: Semistructured interviews with 40 practitioners and policymakers elicited value propositions, capabilities required for successful open data programs, and strategies for improving impact and sustainability. Transcripts were analyzed using a grounded theory approach to identify common perspectives and divergent viewpoints. Results: Respondents were optimistic about the value of open data, reporting numerous opportunities to advance the triple aim of lower costs, improved health care quality, and better population health. Benefits to agencies include enhanced data quality and more efficient operations. External benefits include improved health literacy, data-driven changes in health care delivery, consumer engagement, and community empowerment. Key challenges are resources, cultural resistance, navigating legal and regulatory issues, and data quality. Discussion: The open data movement will likely continue, but success requires sustained leadership, resources, organizational cultural change, promotion of data use, and governance. Jurisdictions that are initiating open data programs can incorporate these lessons from early innovators. Conclusions: The open data movement has a bright future but unknown long-term impact. To maintain momentum, important directions for the field include reconsidering legal guidance on protecting health data in the open data era and quantifying the return on investment.

Knowledge and Practices of Infection Prevention and Control towards COVID-19 among Healthcare Workers in Benghazi Medical Centre, Libya

2021 ◽  
pp. 30-42
Author(s):  
Naeima Houssein
Keyword(s):  
Health Care ◽  
Health Care Workers ◽  
Prevention And Control ◽  
Healthcare Workers ◽  
Infection Prevention ◽  
Good Practice ◽  
Medical Centre ◽  
Infection Prevention And Control ◽  
Care Workers ◽  
And Control

Background: The current pandemic puts a substantial pressure on the health-care system worldwide. Healthcare workers especially those in frontline of patients’ care are at increased risk of being infected. The aim of this study is to assess infection prevention and control knowledge and practice toward COVID- 19 among health care workers. Methods: A cross-sectional study was conducted from July to September of 2020 at Benghazi Medical Centre in Benghazi, Libya. Self-administered questionnaire was distributed to 400 health care workers. Results: Of those surveyed, the majority of participants were females; (40 .6%) in frontline, the overall percentage of correct answers was (64.8%). Knowledge was gained mainly from news media by (27.8%) of participants, while official government websites were used by nearly 21% of participants. The percentage of total good practice score was (76.28%). The knowledge scores were significantly associated with differences by gender, occupation, and level of education (p<0.05). Conclusion: The study found a satisfactory knowledge and relatively good practice towards COVID-19 among health care workers. However; there were areas with poor knowledge and practices. These areas should be addressed through continuous public health education on COVID-19 infection prevention and control.

scholarly journals Patient-Generated Health Data Quality for Clinical Use: Human and Technology Factors (Preprint)

2018 ◽  
Author(s):  
Robab Abdolkhani ◽  
Kathleen Gray ◽  
Ann Borda ◽  
Ruth De Souza
Keyword(s):  
Health Care ◽  
Human Factors ◽  
Data Quality ◽  
Real Time ◽  
Health Care Providers ◽  
Health Data ◽  
Clinical Use ◽  
Care Providers ◽  
Quality Dimensions

BACKGROUND The proliferation of advanced wearable medical technologies is increasing the production of Patient-Generated Health Data (PGHD). However, there is lack of evidence on whether the quality of the data generated from wearables can be effectively used for patient care. In order for PGHD to be utilized for decision making by health providers, it needs to be of high quality, that is, it must comply with standards defined by health care organizations and be accurate, consistent, complete and unbiased. Although medical wearables record highly accurate data, there are other technology issues as well as human factors that affect PGHD quality when it is collected and shared under patients’ control to ultimately used by health care providers. OBJECTIVE This paper explores human factors and technology factors that impact on the quality of PGHD from medical wearables for effective use in clinical care. METHODS We conducted semi-structured interviews with 17 PGHD stakeholders in Australia, the US, and the UK. Participants include ten health care providers working with PGHD from medical wearables in diabetes, sleep disorders, and heart arrhythmia, five health IT managers, and two executives. The participants were interviewed about seven data quality dimensions including accuracy, accessibility, coherence, institutional environment, interpretability, relevancy, and timeliness. Open coding of the interview data identified several technology and human issues related to the data quality dimensions regarding the clinical use of PGHD. RESULTS The overarching technology issues mentioned by participants include lack of advanced functionalities such as real-time alerts for patients as well as complicated settings which can result in errors. In terms of PGHD coherence, different wearables have different data capture mechanisms for the same health condition that create different formats which result in difficult PGHD interpretation and comparison. Another technology issue that is relevant to the current ICT infrastructure of the health care settings is lack of possibility in real-time PGHD access by health care providers which reduce the value of PGHD use. Besides, health care providers addressed a challenge on where PGHD is stored and who truthfully owns the data that affect the feasibility of PGHD access. The human factors included a lack of digital health literacy among patients which shape both the patients’ motivation and their behaviors toward PGHD collection. For example, the gaps in data recording shown in the results indicate the wearable was not used for a time duration. Participants also identified the cost of devices as a barrier to the long-term engagement and use of wearables. CONCLUSIONS Using PGHD garnered from medical wearables is problematic in clinical contexts due to low-quality data influenced by technology and human factors. At present, no guidelines have been defined to assess PGHD quality. Hence, there is a need for new solutions to overcome the existing technology and human-related barriers to enhance PGHD quality.

scholarly journals Quality of Big Data in health care

2015 ◽  
Vol 28 (6) ◽  
pp. 621-634 ◽  
Author(s):  
Sreenivas R. Sukumar ◽  
Ramachandran Natarajan ◽  
Regina K. Ferrell
Keyword(s):  
Health Care ◽  
Big Data ◽  
Data Quality ◽  
Health Data ◽  
Data Sets ◽  
Processing Technologies ◽  
Content Type ◽  
Road Map ◽  
Quality Issues ◽  
Health Care Analytics

Purpose – The current trend in Big Data analytics and in particular health information technology is toward building sophisticated models, methods and tools for business, operational and clinical intelligence. However, the critical issue of data quality required for these models is not getting the attention it deserves. The purpose of this paper is to highlight the issues of data quality in the context of Big Data health care analytics. Design/methodology/approach – The insights presented in this paper are the results of analytics work that was done in different organizations on a variety of health data sets. The data sets include Medicare and Medicaid claims, provider enrollment data sets from both public and private sources, electronic health records from regional health centers accessed through partnerships with health care claims processing entities under health privacy protected guidelines. Findings – Assessment of data quality in health care has to consider: first, the entire lifecycle of health data; second, problems arising from errors and inaccuracies in the data itself; third, the source(s) and the pedigree of the data; and fourth, how the underlying purpose of data collection impact the analytic processing and knowledge expected to be derived. Automation in the form of data handling, storage, entry and processing technologies is to be viewed as a double-edged sword. At one level, automation can be a good solution, while at another level it can create a different set of data quality issues. Implementation of health care analytics with Big Data is enabled by a road map that addresses the organizational and technological aspects of data quality assurance. Practical implications – The value derived from the use of analytics should be the primary determinant of data quality. Based on this premise, health care enterprises embracing Big Data should have a road map for a systematic approach to data quality. Health care data quality problems can be so very specific that organizations might have to build their own custom software or data quality rule engines. Originality/value – Today, data quality issues are diagnosed and addressed in a piece-meal fashion. The authors recommend a data lifecycle approach and provide a road map, that is more appropriate with the dimensions of Big Data and fits different stages in the analytical workflow.

scholarly journals Clinicians and Older Adults’ Perceptions of the Utility of Patient-Generated Health Data in Caring for Older Adults: Exploratory Mixed Methods Study (Preprint)

2021 ◽  
Author(s):  
Ben Kim ◽  
Peyman Ghasemi ◽  
Paul Stolee ◽  
Joon Lee
Keyword(s):  
Older Adults ◽  
Health Care ◽  
Mixed Methods ◽  
Focus Group ◽  
Data Quality ◽  
Health Data ◽  
Perceived Usefulness ◽  
Mixed Methods Study ◽  
Focus Group Interviews ◽  
Group Interviews

BACKGROUND Many people are motivated to self-track their health and optimize their well-being through mobile health apps and wearable devices. The diversity and complexity of these systems have evolved over time, resulting in a large amount of data referred to as patient-generated health data (PGHD), which has recently emerged as a useful set of data elements in health care systems around the world. Despite the increased interest in PGHD, clinicians and older adults’ perceptions of PGHD are poorly understood. In particular, although some clinician barriers to using PGHD have been identified, such as concerns about data quality, ease of use, reliability, privacy, and regulatory issues, little is known from the perspectives of older adults. OBJECTIVE This study aims to explore the similarities and differences in the perceptions of older adults and clinicians with regard to how various types of PGHD can be used to care for older adults. METHODS A mixed methods study was conducted to explore clinicians and older adults’ perceptions of PGHD. Focus groups were conducted with older adults and health care providers from the Greater Toronto area and the Kitchener-Waterloo region. The participants were asked to discuss their perceptions of PGHD, including facilitators and barriers. A questionnaire aimed at exploring the perceived usefulness of a range of different PGHD was also embedded in the study design. Focus group interviews were transcribed for thematic analysis, whereas the questionnaire results were analyzed using descriptive statistics. RESULTS Of the 9 participants, 4 (44%) were clinicians (average age 38.3 years, SD 7 years), and 5 (56%) were older adults (average age 81.0 years, SD 9.1 years). Four main themes were identified from the focus group interviews: influence of PGHD on patient-provider trust, reliability of PGHD, meaningful use of PGHD and PGHD-based decision support systems, and perceived clinical benefits and intrusiveness of PGHD. The questionnaire results were significantly correlated with the frequency of PGHD mentioned in the focus group interviews (<i>r</i>=0.42; <i>P</i>=.03) and demonstrated that older adults and clinicians perceived blood glucose, step count, physical activity, sleep, blood pressure, and stress level as the most useful data for managing health and delivering high-quality care. CONCLUSIONS This embedded mixed methods study generated several important findings about older adults and clinicians’ perceptions and perceived usefulness of a range of PGHD. Owing to the exploratory nature of this study, further research is needed to understand the concerns about data privacy, potential negative impact on the trust between older adults and clinicians, data quality and quantity, and usability of PGHD-related technologies for older adults.

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