Defining and measuring completeness of electronic health records for secondary use

Weiskopf, Nicole G.; Hripcsak, George; Swaminathan, Sushmita; Weng, Chunhua

doi:10.1016/j.jbi.2013.06.010

Cited by 298 publications

(222 citation statements)

References 33 publications

Supporting

Mentioning

215

Contrasting

Unclassified

Order By: Relevance

“…Improving accuracy of data in the health record: Medical records can be inaccurate, incomplete, and biased. [65] With increased patient demand for access to medical records [8] and increased incentives to provide that access in recent federal legislation [7], more patients may review their medical records and help correct mistakes they discover [66]. Furthermore, this practice may motivate clinicians to improve data collection and documentation.…”

Section: Benefitsmentioning

confidence: 99%

Patient Experiences Using an Inpatient Personal Health Record

Woollen¹,

Prey²,

Wilcox³

et al. 2016

Appl Clin Inform

Self Cite

View full text Add to dashboard Cite

SummaryObjective: To investigate patients' experience using an inpatient personal health record (PHR) on a tablet computer to increase engagement in their hospital care. Methods: We performed observations and conducted semi-structured interviews with 14 post-operative cardiac surgical patients and their family members who received an inpatient PHR. Themes were identified using an inductive coding scheme. Results: All participants responded favorably to having access to view their clinical information. A majority (85.7%) of participants used the application following an initial training session. Patients reported high satisfaction with being able to view their hospital medications and access educational materials related to their medical conditions. Patients reported a desire to view daily progress reports about their hospital stay and have access to educational information about their postacute recovery. In addition, patients expressed a common desire to view their diagnoses, laboratory test results, radiology reports, and procedure notes in language that is patient-friendly. Conclusion:Patients have unmet information needs in the hospital setting. Our findings suggest that for some inpatients and their family members, providing personalized health information through a tablet computer may improve satisfaction, decrease anxiety, increase understanding of their health conditions, and improve safety and quality of care. BackgroundBeing a patient in the hospital is an anxiety-inducing experience. Poor communication and lack of access to information can have detrimental effects on a patient's psychological wellbeing and coping abilities [1][2][3][4][5]. Policymakers [6, 7] and patient advocates [8,9] are increasingly focusing on this problem. This study investigates how an inpatient personal health record (PHR) may help address the information needs of patients and improve their engagement and experience in the hospital.Provision of medical information and education about care can allay a patient's uncertainty and stress [10][11][12][13][14][15]. Further, sharing information has been shown to reduce patient requests for clinicians' time, foster a sense of control and empowerment while promoting self-efficacy and ownership of illness [16][17][18], and lead to increased participation as an informed partner in making health decisions [19][20][21][22]. Previous research suggests that patients want to use PHRs and believe that they will be valuable [23]. Moreover, patients believe that their adherence to treatment regimens will improve if they have the opportunity to read and understand their health information [24,25]. However, patients are not typically given the opportunity to review their health information in the hospital. For example, in a survey of hospitalized patients conducted by Cumbler and colleagues, 90% of respondents said they wanted to review their hospital medication list for accuracy, but only 28% said they were given the opportunity to do so [26]. Without access to a documented care plan, patients ar...

show abstract

Section: Benefitsmentioning

confidence: 99%

Patient Experiences Using an Inpatient Personal Health Record

Woollen¹,

Prey²,

Wilcox³

et al. 2016

Appl Clin Inform

Self Cite

View full text Add to dashboard Cite

show abstract

“…[10][11][12][13][14] This is a well-recognized problem; numerous efforts have been made to establish techniques to validate this data source. 12,[15][16][17][18][19][20] In a review of 35 empirical studies, Chan, Fowles, and Weiner found a substantial lack of agreement regarding which data quality (DQ) dimensions were important to assess. 12 The authors discovered that, of the included studies, "66 percent assessed accuracy, 57 percent completeness, and 23 percent data comparability."…”

Section: Introductionmentioning

confidence: 99%

A Comparison of Data Quality Assessment Checks in Six Data Sharing Networks

Callahan¹,

Bauck²,

Bertoch³

et al. 2017

eGEMs

View full text Add to dashboard Cite

Objective: To compare rule-based data quality (DQ) assessment approaches across multiple national clinical data sharing organizations. Methods:Six organizations with established data quality assessment (DQA) programs provided documentation or source code describing current DQ checks. DQ checks were mapped to the categories within the data verification context of the harmonized DQA terminology. To ensure all DQ checks were consistently mapped, conventions were developed and four iterations of mapping performed. Difficultto-map DQ checks were discussed with research team members until consensus was achieved.Results: Participating organizations provided 11,026 DQ checks, of which 99.97 percent were successfully mapped to a DQA category. Of the mapped DQ checks (N=11,023), 214 (1.94 percent) mapped to multiple DQA categories. The majority of DQ checks mapped to Atemporal Plausibility (49.60 percent), Value Conformance (17.84 percent), and Atemporal Completeness (12.98 percent) categories. Discussion: Using the common DQA terminology, near-complete (99.97 percent) coverage across a wide range of DQA programs and specifications was reached. Comparing the distributions of mapped DQ checks revealed important differences between participating organizations. This variation may be related to the organization's stakeholder requirements, primary analytical focus, or maturity of their DQA program. Not within scope, mapping checks within the data validation context of the terminology may provide additional insights into DQA practice differences. Conclusion:A common DQA terminology provides a means to help organizations and researchers understand the coverage of their current DQA efforts as well as highlight potential areas for additional DQA development. Sharing DQ checks between organizations could help expand the scope of DQA across clinical data networks.

show abstract

“…Quality Knowledge Repository (QKR): We extracted DQC, their definitions and applicable measures, their relationships, and the computability of DQC in existing DQF (e.g. Kahn 15 , Weiskopf 18 from literature. We identified primitives existing in different DQF to develop a DQ metamodel and implemented it as the QKR.…”

Section: A Service Oriented Architecture For Assessing Quality Of Hetmentioning

confidence: 99%

Methods for examining data quality in healthcare integrated data repositories

et al. 2017

View full text Add to dashboard Cite

This paper summarizes content of the workshop focused on data quality. The first speaker (VH) described data quality infrastructure and data quality evaluation methods currently in place within the Observational Data Science and Informatics (OHDSI) consortium. The speaker described in detail a data quality tool called Achilles Heel and latest development for extending this tool. Interim results of an ongoing Data Quality study within the OHDSI consortium were also presented. The second speaker (MK) described lessons learned and new data quality checks developed by the PEDsNet pediatric research network. The last two speakers (JB, RG) described tools developed by the Sentinel Initiative and University of Utah's service oriented framework. The workshop discussed at the end and throughout how data quality assessment can be advanced by combining best features of each network.

show abstract

Defining and measuring completeness of electronic health records for secondary use

Cited by 298 publications

References 33 publications

Patient Experiences Using an Inpatient Personal Health Record

Patient Experiences Using an Inpatient Personal Health Record

A Comparison of Data Quality Assessment Checks in Six Data Sharing Networks

Methods for examining data quality in healthcare integrated data repositories

Contact Info

Product

Resources

About