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We work with them? Healthcare workers interpretation of organizational relations mined from electronic health records.

ABSTRACT: Models of healthcare organizations (HCOs) are often defined up front by a select few administrative officials and managers. However, given the size and complexity of modern healthcare systems, this practice does not scale easily. The goal of this work is to investigate the extent to which organizational relationships can be automatically learned from utilization patterns of electronic health record (EHR) systems.We designed an online survey to solicit the perspectives of employees of a large academic medical center. We surveyed employees from two administrative areas: (1) Coding & Charge Entry and (2) Medical Information Services and two clinical areas: (3) Anesthesiology and (4) Psychiatry. To test our hypotheses we selected two administrative units that have work-related responsibilities with electronic records; however, for the clinical areas we selected two disciplines with very different patient responsibilities and whose accesses and people who accessed were similar. We provided each group of employees with questions regarding the chance of interaction between areas in the medical center in the form of association rules (e.g., Given someone from Coding & Charge Entry accessed a patient's record, what is the chance that someone from Medical Information Services access the same record?). We compared the respondent predictions with the rules learned from actual EHR utilization using linear-mixed effects regression models.The findings from our survey confirm that medical center employees can distinguish between association rules of high and non-high likelihood when their own area is involved. Moreover, they can make such distinctions between for any HCO area in this survey. It was further observed that, with respect to highly likely interactions, respondents from certain areas were significantly better than other respondents at making such distinctions and certain areas' associations were more distinguishable than others.These results illustrate that EHR utilization patterns may be consistent with the expectations of HCO employees. Our findings show that certain areas in the HCO are easier than others for employees to assess, which suggests that automated learning strategies may yield more accurate models of healthcare organizations than those based on the perspectives of a select few individuals.

SUBMITTER: Chen Y

PROVIDER: S-EPMC4159755 | biostudies-literature | 2014 Jul

REPOSITORIES: biostudies-literature

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We work with them? Healthcare workers interpretation of organizational relations mined from electronic health records.

Chen You Y Lorenzi Nancy N Nyemba Steve S Schildcrout Jonathan S JS Malin Bradley B

International journal of medical informatics 20140428 7

<h4>Objective</h4>Models of healthcare organizations (HCOs) are often defined up front by a select few administrative officials and managers. However, given the size and complexity of modern healthcare systems, this practice does not scale easily. The goal of this work is to investigate the extent to which organizational relationships can be automatically learned from utilization patterns of electronic health record (EHR) systems.<h4>Method</h4>We designed an online survey to solicit the perspec ...[more]

PMID: 24845147

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Project description:BACKGROUND:Organizational justice is the first virtue in social institutions (J Manage 16:399-432, 1990). It is one of the most determinant factors for an effective utilization of human resources and an essential predictor of organizational success (J Manag Dev 28:457-477, 2009). Employees who perceive fairness are more likely happy with their job and less likely leave their organization (Int J Bus Manage 4:145-154, 2009). Perceived injustice, on the other hand, diminishes motivation of workers to accomplish their duties (Int J Bus Manage 4:145-154, 2009; J Educ Sci Univ Tabriz 2:27-34, 2009). Ethiopia has given emphasis to the expansion of health institutions and increasing the number of health professionals. Despite this, little emphasis has been given the human resource aspect of the health sector. Therefore, this study aims to investigate organizational justice perceptions and turnover intentions among healthcare workers in Amhara region. METHODS:One hundred ninety seven healthcare workers participated in the study. Data were collected through self- report questionnaire and semi-structured interview. The quantitative data were analyzed through MANOVA, multiple regression, and independent samples t-test. The qualitative data were analyzed through thematic analysis. RESULTS:The results of this study revealed that healthcare workers in the public hospitals held low perceived distributive, procedural, interpersonal and informational justice. Similarly, private hospitals healthcare workers had low perceptions on distributive and procedural justice. On the contrary, healthcare workers in private hospitals reported high perception of fairness on interpersonal and informational justice aspects. Both public and private hospital healthcare workers had high turnover intention. The result revealed significant difference in organizational justice perceptions between private and public hospital healthcare workers (F (4, 182) = 9.17; p < .05; partial η2 =. 168). Organizational justice dimensions (distributive, procedural, interpersonal and informational justice) significantly contributed an additional 9.9% variation in turnover intention (R 2 change = .099, F (4,170) = 4.86, p < .05). Distributive justice was the most important predictor of turnover intention (β = -.23, p < .05). CONCLUSION:Organizational justice perceptions of healthcare workers significantly predicted turnover intention. Hence, organizational justice should be given due emphasis in designing and implementing policies and strategies of human resource management.

Project description:ImportanceElectronic health records (EHRs) are considered a potentially significant contributor to clinician burnout.ObjectiveTo describe the association of EHR usage, sex, and work culture with burnout for 3 types of clinicians at an academic medical institution.Design, setting, and participantsThis cross-sectional study of 1310 clinicians at a large tertiary care academic medical center analyzed EHR usage metrics for the month of April 2019 with results from a well-being survey from May 2019. Participants included attending physicians, advanced practice providers (APPs), and house staff from various specialties. Data were analyzed between March 2020 and February 2021.ExposuresClinician demographic characteristics, EHR metadata, and an institution-wide survey.Main outcomes and measuresStudy metrics included clinician demographic data, burnout score, well-being measures, and EHR usage metadata.ResultsOf the 1310 clinicians analyzed, 542 (41.4%) were men (mean [SD] age, 47.3 [11.6] years; 448 [82.7%] White clinicians, 52 [9.6%] Asian clinicians, and 21 [3.9%] Black clinicians) and 768 (58.6%) were women (mean [SD] age, 42.6 [10.3] years; 573 [74.6%] White clinicians, 105 [13.7%] Asian clinicians, and 50 [6.5%] Black clinicians). Women reported more burnout (survey score ≥50: women, 423 [52.0%] vs men, 258 [47.6%]; P = .008) overall. No significant differences in EHR usage were found by sex for multiple metrics of time in the EHR, metrics of volume of clinical encounters, or differences in products of clinical care. Multivariate analysis of burnout revealed that work culture domains were significantly associated with self-reported results for commitment (odds ratio [OR], 0.542; 95% CI, 0.427-0.688; P < .001) and work-life balance (OR, 0.643; 95% CI, 0.559-0.739; P < .001). Clinician sex significantly contributed to burnout, with women having a greater likelihood of burnout compared with men (OR, 1.33; 95% CI, 1.01-1.75; P = .04). An increased number of days spent using the EHR system was associated with less likelihood of burnout (OR, 0.966; 95% CI, 0.937-0.996; P = .03). Overall, EHR metrics accounted for 1.3% of model variance (P = .001) compared with work culture accounting for 17.6% of variance (P < .001).Conclusions and relevanceIn this cross-sectional study, sex-based differences in EHR usage and burnout were found in clinicians. These results also suggest that local work culture factors may contribute more to burnout than metrics of EHR usage.

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We work with them? Healthcare workers interpretation of organizational relations mined from electronic health records.

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We work with them? Healthcare workers interpretation of organizational relations mined from electronic health records.

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