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8021. A-20. Measure 17: Referral rate for intensive counseling from a community program | Agency for Healthcare Research and Quality
      www.ahrq.gov/prevention/resources/chronic-care/ccrm-atlas-suppl/ccrm-apptabA-20.html
      October 01, 2013 - Potential Measures for Clinical-Community Relationships A-20. Measure 17: Referral rate for intensive counseling from a community program Previous Page Next Page Table of Contents Potential Measures for Clinical-Community Relationships Acknowledgements Executive Summary Introduction Potentia…

8022. Learning from patients' experiences related to diagnostic errors is essential for progress in patient safety. | PSNet
      psnet.ahrq.gov/issue/learning-patients-experiences-related-diagnostic-errors-essential-progress-patient-safety
      May 20, 2020 - Study Emerging Classic Learning from patients' experiences related to diagnostic errors is essential for progress in patient safety. Citation Text: Giardina TD, Haskell H, Menon S, et al. Learning From Patients' Experiences Related To Diagnostic Errors Is Essent…

8023. Electronic trigger-based intervention to reduce delays in diagnostic evaluation for cancer: a cluster randomized controlled trial. | PSNet
      psnet.ahrq.gov/issue/electronic-trigger-based-intervention-reduce-delays-diagnostic-evaluation-cancer-cluster
      April 09, 2013 - Study Classic Electronic trigger-based intervention to reduce delays in diagnostic evaluation for cancer: a cluster randomized controlled trial. Citation Text: Murphy DR, Wu L, Thomas EJ, et al. Electronic Trigger-Based Intervention to Reduce Delays in Diagnosti…

8024. Influence of gender, profession, and managerial function on clinicians' perceptions of patient safety culture: a cross-national cross-sectional study. | PSNet
      psnet.ahrq.gov/issue/influence-gender-profession-and-managerial-function-clinicians-perceptions-patient-safety
      September 07, 2022 - Study Influence of gender, profession, and managerial function on clinicians' perceptions of patient safety culture: a cross-national cross-sectional study. Citation Text: Gambashidze N, Hammer A, Wagner A, et al. Influence of gender, profession, and managerial function on clinicians' pe…

8025. Significant reduction in preanalytical errors for nonphlebotomy blood draws after implementation of a novel integrated specimen collection module. | PSNet
      psnet.ahrq.gov/issue/significant-reduction-preanalytical-errors-nonphlebotomy-blood-draws-after-implementation
      May 29, 2019 - Study Significant reduction in preanalytical errors for nonphlebotomy blood draws after implementation of a novel integrated specimen collection module. Citation Text: Le RD, Melanson SEF, Petrides AK, et al. Significant Reduction in Preanalytical Errors for Nonphlebotomy Blood Draws Aft…

8026. Strengthening Patient Engagement to Improve Care and Shared Decision Making - Emerging Research | Digital Healthcare Research
      digital.ahrq.gov/2020-year-review/research-summary/strengthening-patient-engagement-improve-care-and-shared-decision-making-emerging-research
      January 01, 2020 - Strengthening Patient Engagement to Improve Care and Shared Decision Making - Emerging Research Using Technology to Support Patient-Centered, Shared Decision Making in Care and Treatment Decisions Patient-centered shared decision making refers to the collaborative effort of a healthc…

8027. Errors in palliative care: kinds, causes, and consequences: a pilot survey of experiences and attitudes of palliative care professionals. | PSNet
      psnet.ahrq.gov/issue/errors-palliative-care-kinds-causes-and-consequences-pilot-survey-experiences-and-attitudes
      December 04, 2016 - Study Errors in palliative care: kinds, causes, and consequences: a pilot survey of experiences and attitudes of palliative care professionals. Citation Text: Dietz I, Borasio GD, Molnar C, et al. Errors in palliative care: kinds, causes, and consequences: a pilot survey of experiences a…

8028. Artificial intelligence versus clinicians: systematic review of design, reporting standards, and claims of deep learning studies. | PSNet
      psnet.ahrq.gov/issue/artificial-intelligence-versus-clinicians-systematic-review-design-reporting-standards-and
      May 20, 2019 - Review Classic Artificial intelligence versus clinicians: systematic review of design, reporting standards, and claims of deep learning studies. Citation Text: Nagendran M, Chen Y, Lovejoy CA, et al. Artificial intelligence versus clinicians: systematic review o…

8029. Nurses' perceptions and demands regarding COVID-19 care delivery in critical care units and hospital emergency services. | PSNet
      psnet.ahrq.gov/issue/nurses-perceptions-and-demands-regarding-covid-19-care-delivery-critical-care-units-and
      March 09, 2022 - Study Emerging Classic Nurses' perceptions and demands regarding COVID-19 care delivery in critical care units and hospital emergency services. Citation Text: González-Gil MT, González-Blázquez C, Parro-Moreno AI, et al. Nurses’ perceptions and demands regarding…

8030. A patient feedback reporting tool for OpenNotes: implications for patient–clinician safety and quality partnerships. | PSNet
      psnet.ahrq.gov/issue/patient-feedback-reporting-tool-opennotes-implications-patient-clinician-safety-and-quality
      June 06, 2018 - Study A patient feedback reporting tool for OpenNotes: implications for patient–clinician safety and quality partnerships. Citation Text: Bell SK, Gerard M, Fossa A, et al. A patient feedback reporting tool for OpenNotes: implications for patient-clinician safety and quality partnerships…

8031. Patient engagement in the inpatient setting: a systematic review. | PSNet
      psnet.ahrq.gov/issue/patient-engagement-inpatient-setting-systematic-review
      November 02, 2018 - Review Patient engagement in the inpatient setting: a systematic review. Citation Text: Prey JE, Woollen J, Wilcox L, et al. Patient engagement in the inpatient setting: a systematic review. J Am Med Inform Assoc. 2014;21(4):742-750. doi:10.1136/amiajnl-2013-002141. Copy Citation F…

8032. Device Dashboard Data 2025 (xls file)
      www.ahrq.gov/sites/default/files/wysiwyg/npsd/Device_Dashboard_Data_2025.xlsx
      January 01, 2025 - Introduction Introduction-Device or Medical/Surgical Supply including Health Information Technology   The tables in this workbook present data on Device or Medical/Surgical Supply reports submitted by AHRQ-listed Patient Safety Organizations (PSOs) to the Network of Patient Safety Databases (NPSD) through December 3…

8033. A machine learning-based clinical decision support system to identify prescriptions with a high risk of medication error. | PSNet
      psnet.ahrq.gov/issue/machine-learning-based-clinical-decision-support-system-identify-prescriptions-high-risk
      May 20, 2020 - Study Emerging Classic A machine learning-based clinical decision support system to identify prescriptions with a high risk of medication error. Citation Text: Corny J, Rajkumar A, Martin O, et al. A machine learning–based clinical decision support system to ide…

8034. Emergency Department | Digital Healthcare Research
      digital.ahrq.gov/care-setting/emergency-department
      January 01, 2023 - Emergency Department Disseminating and Implementing MedSMA℞T Families in Emergency Departments: A Randomized Control Trial to Assess Effectiveness of an Evidence-Based Gaming Intervention to Reduce Opioid Misuse Description This research tests the effectiveness of MedSMA℞T Mob…

8035. Repurposing clinical decision support system data to measure dosing errors and clinician-level quality of care. | PSNet
      psnet.ahrq.gov/issue/repurposing-clinical-decision-support-system-data-measure-dosing-errors-and-clinician-level
      October 21, 2020 - Study Repurposing clinical decision support system data to measure dosing errors and clinician-level quality of care. Citation Text: Chin DL, Wilson MH, Trask AS, et al. Repurposing clinical decision support system data to measure dosing errors and clinician-level quality of care. J Med …

8036. Improving quality and safety of care using "technovigilance": an ethnographic case study of secondary use of data from an electronic prescribing and decision support system. | PSNet
      psnet.ahrq.gov/issue/improving-quality-and-safety-care-using-technovigilance-ethnographic-case-study-secondary-use
      March 05, 2014 - Study Improving quality and safety of care using "technovigilance": an ethnographic case study of secondary use of data from an electronic prescribing and decision support system. Citation Text: Dixon-Woods M, Redwood S, Leslie M, et al. Improving quality and safety of care using "techno…

8037. Combined proactive risk assessment: unifying proactive and reactive risk assessment techniques in health care. | PSNet
      psnet.ahrq.gov/issue/combined-proactive-risk-assessment-unifying-proactive-and-reactive-risk-assessment-techniques
      May 11, 2022 - Study Combined proactive risk assessment: unifying proactive and reactive risk assessment techniques in health care. Citation Text: Bender JA, Kulju S, Soncrant C. Combined proactive risk assessment: unifying proactive and reactive risk assessment techniques in health care. Jt Comm J Qua…

8038. The barriers and enhancers to trust in a just culture in hospital settings: a systematic review. | PSNet
      psnet.ahrq.gov/issue/barriers-and-enhancers-trust-just-culture-hospital-settings-systematic-review
      February 02, 2022 - Review The barriers and enhancers to trust in a just culture in hospital settings: a systematic review. Citation Text: van Marum S, Verhoeven D, de Rooy D. The barriers and enhancers to trust in a just culture in hospital settings: a systematic review. J Patient Saf. 2022;18(7):e1067-e10…

8039. Safe opioid prescribing: a prognostic machine learning approach to predicting 30-day risk after an opioid dispensation in Alberta, Canada. | PSNet
      psnet.ahrq.gov/issue/safe-opioid-prescribing-prognostic-machine-learning-approach-predicting-30-day-risk-after
      July 22, 2020 - Study Safe opioid prescribing: a prognostic machine learning approach to predicting 30-day risk after an opioid dispensation in Alberta, Canada. Citation Text: Sharma V, Kulkarni V, Eurich DT, et al. Safe opioid prescribing: a prognostic machine learning approach to predicting 30-day ris…

8040. Psychosocial working conditions as determinants of concerns to have made important medical errors and possible intermediate factors of this association among medical assistants - a cohort study. | PSNet
      psnet.ahrq.gov/issue/psychosocial-working-conditions-determinants-concerns-have-made-important-medical-errors-and
      July 13, 2022 - Study Psychosocial working conditions as determinants of concerns to have made important medical errors and possible intermediate factors of this association among medical assistants - a cohort study. Citation Text: Mambrey V, Angerer P, Loerbroks A. Psychosocial working conditions as de…