National Healthcare Quality and Disparities Report
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AHRQ Research Studies Date
Topics
- Adverse Drug Events (ADE) (5)
- Adverse Events (5)
- Catheter-Associated Urinary Tract Infection (CAUTI) (1)
- Children/Adolescents (6)
- (-) Clinical Decision Support (CDS) (39)
- Critical Care (1)
- Data (2)
- Decision Making (15)
- Diagnostic Safety and Quality (2)
- Disabilities (1)
- Elderly (1)
- Electronic Health Records (EHRs) (9)
- Electronic Prescribing (E-Prescribing) (1)
- Emergency Department (1)
- Emergency Medical Services (EMS) (2)
- Evidence-Based Practice (1)
- Falls (1)
- Genetics (1)
- Guidelines (1)
- Healthcare-Associated Infections (HAIs) (1)
- Healthcare Delivery (1)
- Health Information Exchange (HIE) (1)
- Health Information Technology (HIT) (19)
- Heart Disease and Health (1)
- Hospitalization (2)
- Hospitals (3)
- Human Immunodeficiency Virus (HIV) (2)
- Imaging (2)
- Infectious Diseases (2)
- Injuries and Wounds (2)
- Inpatient Care (4)
- Long-Term Care (1)
- Medical Errors (3)
- Medication (7)
- Medication: Safety (1)
- Mortality (1)
- Newborns/Infants (1)
- Nursing (1)
- Nursing Homes (2)
- Nutrition (1)
- Patient-Centered Outcomes Research (1)
- Patient Adherence/Compliance (1)
- Patient Safety (14)
- Practice Patterns (1)
- Pressure Ulcers (2)
- Prevention (4)
- Primary Care (4)
- Provider (1)
- Provider: Health Personnel (1)
- Provider: Nurse (1)
- Provider: Physician (1)
- Public Health (1)
- Quality Measures (1)
- Quality of Care (2)
- Respiratory Conditions (2)
- Risk (3)
- Sepsis (2)
- Tools & Toolkits (1)
- Urinary Tract Infection (UTI) (1)
AHRQ Research Studies
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Research Studies is a compilation of published research articles funded by AHRQ or authored by AHRQ researchers.
Results
1 to 25 of 39 Research Studies DisplayedWang J, Gong Y
Potential of decision support in preventing pressure ulcers in hospitals.
The development of hospital-acquired pressure ulcers signals low quality of care. To meet the challenges of consistently translating best practices into effective clinical practices and promote effective teamwork communication and interprofessional collaboration, the authors consider the failure of consistent care delivery as loss of information and reveal the opportunities of informatics methods to reinforce information delivery, evidenced by typical cases. They then explain and summarize information-related issues existing at the initial assessment upon hospital admission, routine treatments, and team communication.
AHRQ-funded; HS022895.
Citation: Wang J, Gong Y .
Potential of decision support in preventing pressure ulcers in hospitals.
Stud Health Technol Inform 2017;241:15-20.
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Keywords: Clinical Decision Support (CDS), Decision Making, Hospitals, Patient Safety, Pressure Ulcers, Prevention
Dykes PC, Duckworth M, Cunningham S
Pilot testing Fall TIPS (Tailoring Interventions for Patient Safety): a patient-centered fall prevention toolkit.
Patient falls during an acute hospitalization cause injury, reduced mobility, and increased costs. The laminated paper Fall TIPS Toolkit (Fall TIPS) provides clinical decision support at the bedside by linking each patient's fall risk assessment with evidence-based interventions. The investigators examined strategies to integrate this evidence into clinical practice. They concluded that engaging hospital and clinical leadership is critical in translating evidence-based care into clinical practice. They address and detail barriers to adoption of the protocol to provide guidance for spread to other institutions.
AHRQ-funded; HS025128.
Citation: Dykes PC, Duckworth M, Cunningham S .
Pilot testing Fall TIPS (Tailoring Interventions for Patient Safety): a patient-centered fall prevention toolkit.
Jt Comm J Qual Patient Saf 2017 Aug;43(8):403-13. doi: 10.1016/j.jcjq.2017.05.002..
Keywords: Clinical Decision Support (CDS), Decision Making, Evidence-Based Practice, Falls, Hospitals, Injuries and Wounds, Inpatient Care, Patient Safety, Prevention, Risk, Tools & Toolkits
Roosan D, Weir C, Samore M
Identifying complexity in infectious diseases inpatient settings: an observation study.
This study sought to identify specific complexity-contributing factors in the infectious disease domain and the relationship with the complexity perceived by clinicians. Its factor analysis revealed three factors explaining 47 percent of total variance, namely task interaction and goals, urgency and acuity, and psychosocial behavior. A linear regression analysis showed no statistically significant association between complexity perceived by the physicians and objective complexity.
AHRQ-funded; HS023349.
Citation: Roosan D, Weir C, Samore M .
Identifying complexity in infectious diseases inpatient settings: an observation study.
J Biomed Inform 2017 Jul;71s:S13-s21. doi: 10.1016/j.jbi.2016.10.018.
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Keywords: Clinical Decision Support (CDS), Infectious Diseases, Inpatient Care, Patient Safety
Le P, Martinez KA, Pappas MA
A decision model to estimate a risk threshold for venous thromboembolism prophylaxis in hospitalized medical patients.
To determine a threshold for prophylaxis based on risk of venous thromboembolism, the researchers constructed a decision model with a decision-tree following patients for 3 months after hospitalization, and a lifetime Markov model with 3-month cycles. They found that the prophylaxis threshold was relatively insensitive to low-molecular-weight heparin cost and bleeding risk, but very sensitive to patient age and life expectancy.
AHRQ-funded; HS022883.
Citation: Le P, Martinez KA, Pappas MA .
A decision model to estimate a risk threshold for venous thromboembolism prophylaxis in hospitalized medical patients.
J Thromb Haemost 2017 Jun;15(6):1132-41. doi: 10.1111/jth.13687.
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Keywords: Adverse Events, Clinical Decision Support (CDS), Inpatient Care, Patient Safety, Risk
Ancker JS, Edwards A, Nosal S
Effects of workload, work complexity, and repeated alerts on alert fatigue in a clinical decision support system.
In this study, the investigators tested hypotheses arising from two possible alert fatigue mechanisms: (A) cognitive overload associated with amount of work, complexity of work, and effort distinguishing informative from uninformative alerts, and (B) desensitization from repeated exposure to the same alert over time. The investigators found that clinicians became less likely to accept alerts as they received more of them, particularly more repeated alerts. There was no evidence of an effect of workload per se, or of desensitization over time for a newly deployed alert.
AHRQ-funded; HS021531.
Citation: Ancker JS, Edwards A, Nosal S .
Effects of workload, work complexity, and repeated alerts on alert fatigue in a clinical decision support system.
BMC Med Inform Decis Mak 2017 Apr 10;17(1):1-9. doi: 10.1186/s12911-017-0430-8..
Keywords: Clinical Decision Support (CDS), Decision Making, Electronic Health Records (EHRs), Health Information Technology (HIT), Patient Safety, Provider, Provider: Nurse, Provider: Physician
Bhattacharjee P, Edelson DP, Churpek MM
Identifying patients with sepsis on the hospital wards.
The goal of this review was to discuss recent advances in the detection of sepsis in patients on the hospital wards. The investigators discuss data highlighting the benefits and limitations of the systemic inflammatory response syndrome (SIRS) criteria for screening patients with sepsis, such as its low specificity, as well as newly described scoring systems, including the proposed role of the quick sepsis-related organ failure assessment (qSOFA) score.
AHRQ-funded; HS000078.
Citation: Bhattacharjee P, Edelson DP, Churpek MM .
Identifying patients with sepsis on the hospital wards.
Chest 2017 Apr;151(4):898-907. doi: 10.1016/j.chest.2016.06.020..
Keywords: Clinical Decision Support (CDS), Diagnostic Safety and Quality, Hospitalization, Sepsis
Dunn Lopez K, Gephart SM, Raszewski R
Integrative review of clinical decision support for registered nurses in acute care settings.
To report on the state of the science of clinical decision support (CDS) for hospital bedside nurses, the researchers performed an integrative review of qualitative and quantitative peer-reviewed original research studies. They concluded that clinical support systems targeting bedside nurses have positive effects on outcomes and hold promise for improving care quality.
AHRQ-funded; HS022908.
Citation: Dunn Lopez K, Gephart SM, Raszewski R .
Integrative review of clinical decision support for registered nurses in acute care settings.
J Am Med Inform Assoc 2017 Mar 1;24(2):441-50. doi: 10.1093/jamia/ocw084.
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Keywords: Critical Care, Clinical Decision Support (CDS), Health Information Technology (HIT), Nursing, Patient Safety
Romagnoli KM, Nelson SD, Hines L
Information needs for making clinical recommendations about potential drug-drug interactions: a synthesis of literature review and interviews.
To better understand the information needs and work practices of specialists who search and synthesize potential drug-drug interactions (PDDIs) evidence for drug information resources, the researchers conducted an inquiry that combined a thematic analysis of published literature with unstructured interviews. Their review of 92 papers and 10 interviews identified 56 categories of information needs related to the interpretation of PDDI information including drug and interaction information, study design and evidence including clinical details.
AHRQ-funded; HS019461.
Citation: Romagnoli KM, Nelson SD, Hines L .
Information needs for making clinical recommendations about potential drug-drug interactions: a synthesis of literature review and interviews.
BMC Med Inform Decis Mak 2017 Feb 22;17(1):21. doi: 10.1186/s12911-017-0419-3.
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Keywords: Guidelines, Clinical Decision Support (CDS), Medication, Adverse Drug Events (ADE)
Horsky J, Aarts J, Verheul L
Clinical reasoning in the context of active decision support during medication prescribing.
The purpose of this study was to describe and analyze reasoning patterns of clinicians responding to drug-drug interaction alerts in order to understand the role of patient-specific information in the decision-making process about the risks and benefits of medication therapy. The investigators found that declining an alert suggestion was preceded by sometimes brief but often complex reasoning, prioritizing different aspects of care quality and safety, especially when the perceived risk was higher.
AHRQ-funded; HS021094.
Citation: Horsky J, Aarts J, Verheul L .
Clinical reasoning in the context of active decision support during medication prescribing.
Int J Med Inform 2017 Jan;97:1-11. doi: 10.1016/j.ijmedinf.2016.09.004..
Keywords: Adverse Drug Events (ADE), Adverse Events, Clinical Decision Support (CDS), Decision Making, Electronic Health Records (EHRs), Health Information Technology (HIT), Medical Errors, Medication, Patient Safety
Press A, Khan S, McCullagh L
Avoiding alert fatigue in pulmonary embolism decision support: a new method to examine 'trigger rates.'
The authors developed a new and innovative usability process named 'sensitivity and specificity trigger analysis' (SSTA) as part of a larger project around a pulmonary embolism decision support tool. They explored a unique methodology, SSTA, used to limit inaccurate triggering of a clinical decision support tool prior to integration into the electronic health record. They concluded that their methodology can be applied to other studies aiming to decrease triggering rates and increase adoption rates of previously validated clinical decision support system tools.
AHRQ-funded; HS022061.
Citation: Press A, Khan S, McCullagh L .
Avoiding alert fatigue in pulmonary embolism decision support: a new method to examine 'trigger rates.'
Evid Based Med 2016 Dec;21(6):203-07. doi: 10.1136/ebmed-2016-110440.
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Keywords: Clinical Decision Support (CDS), Respiratory Conditions, Electronic Health Records (EHRs), Provider: Health Personnel, Patient Safety
Roosan D, Samore M, Jones M
Big-data based decision-support systems to improve clinicians' cognition.
This study focused on answers from the experts on how clinical reasoning can be supported by population-based Big-Data. It found cognitive strategies such as trajectory tracking, perspective taking, and metacognition has the potential to improve clinicians' cognition to deal with complex problems. These cognitive strategies all have important implications for the design of Big-Data based decision-support tools that could be embedded in electronic health records.
AHRQ-funded; HS023349.
Citation: Roosan D, Samore M, Jones M .
Big-data based decision-support systems to improve clinicians' cognition.
IEEE Int Conf Healthc Inform 2016;2016:285-88. doi: 10.1109/ichi.2016.39.
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Keywords: Clinical Decision Support (CDS), Decision Making, Data, Electronic Health Records (EHRs)
Islam R, Weir C, Del Fiol G
Clinical complexity in medicine: a measurement model of task and patient complexity.
The objective of this paper is to develop an integrated approach to understand and measure clinical complexity by incorporating both task and patient complexity components focusing on the infectious disease domain. The proposed clinical complexity model consists of two separate components:1) a clinical task complexity model with 13 clinical complexity-contributing factors and 7 dimensions and 2) a patient complexity model with 11 complexity-contributing factors and 5 dimensions.
AHRQ-funded; HS023349.
Citation: Islam R, Weir C, Del Fiol G .
Clinical complexity in medicine: a measurement model of task and patient complexity.
Methods Inf Med 2016;55(1):14-22. doi: 10.3414/me15-01-0031.
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Keywords: Clinical Decision Support (CDS), Decision Making, Health Information Technology (HIT)
Her QL, Amato MG, Seger DL
The frequency of inappropriate nonformulary medication alert overrides in the inpatient setting.
The purpose of this study was to quantify the frequency of inappropriate nonformulary medication (NFM) alert overrides in the inpatient setting and provide insight on how the design of formulary alerts could be improved. The study found that approximately 1 in 5 NFM alert overrides are overridden inappropriately.
AHRQ-funded; HS021094.
Citation: Her QL, Amato MG, Seger DL .
The frequency of inappropriate nonformulary medication alert overrides in the inpatient setting.
J Am Med Inform Assoc 2016 Sep;23(5):924-33. doi: 10.1093/jamia/ocv181..
Keywords: Clinical Decision Support (CDS), Health Information Technology (HIT), Inpatient Care, Medication, Patient Safety
Moore CL, Daniels B, Singh D
Ureteral stones: implementation of a reduced-dose CT protocol in patients in the emergency department with moderate to high likelihood of calculi on the basis of STONE score.
The purpose of this paper was to determine if a reduced-dose computed tomography (CT) protocol could effectively help to identify patients in the emergency department (ED) with moderate to high likelihood of calculi who would require urologic intervention within 90 days. The authors found that a CT protocol with over 85% dose reduction can be used in patients with moderate to high likelihood of ureteral stone to safely and effectively identify patients in the ED who will require urologic intervention.
AHRQ-funded; HS018322.
Citation: Moore CL, Daniels B, Singh D .
Ureteral stones: implementation of a reduced-dose CT protocol in patients in the emergency department with moderate to high likelihood of calculi on the basis of STONE score.
Radiology 2016 Sep;280(3):743-51. doi: 10.1148/radiol.2016151691.
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Keywords: Clinical Decision Support (CDS), Emergency Department, Imaging, Patient Safety
Yin MT, Shiau S, Rimland D
Fracture prediction with modified-FRAX in older HIV-infected and uninfected men.
The authors investigated considering HIV as a cause of secondary osteoporosis when calculating FRAX, a clinical fracture risk calculator, in HIV-infected individuals. They found that modified-FRAX underestimated the fracture rates more in older HIV-infected than in otherwise similar uninfected men. and they recommend further studies to determine how to risk stratify for screening and treatment in older HIV-infected individuals.
AHRQ-funded; HS018372.
Citation: Yin MT, Shiau S, Rimland D .
Fracture prediction with modified-FRAX in older HIV-infected and uninfected men.
J Acquir Immune Defic Syndr 2016 Aug 15;72(5):513-20. doi: 10.1097/qai.0000000000000998.
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Keywords: Clinical Decision Support (CDS), Elderly, Injuries and Wounds, Human Immunodeficiency Virus (HIV), Risk
Bonafide CP, Roland D, Brady PW
Rapid response systems 20 years later: new approaches, old challenges.
In this article, the authors propose a set of recommendations for a research agenda aimed at pursuing the work of optimizing the identification of deteriorating children. They recommend that the second generation of pediatric rapid response systems continue to build on past achievements while further optimizing use of the data, tools, and people available at the bedside to take the next leap forward.
AHRQ-funded; HS023827.
Citation: Bonafide CP, Roland D, Brady PW .
Rapid response systems 20 years later: new approaches, old challenges.
JAMA Pediatr 2016 Aug;170(8):729-30. doi: 10.1001/jamapediatrics.2016.0398.
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Keywords: Children/Adolescents, Clinical Decision Support (CDS), Decision Making, Emergency Medical Services (EMS), Hospitals
Roosan D, Del Fiol G, Butler J
Feasibility of population health analytics and data visualization for decision support in the infectious diseases domain: a pilot study.
The objectives of this study were: 1) to explore the feasibility of extracting and displaying population-based information from an actual clinical population's database records, 2) to explore specific design features for improving population display, 3) to explore perceptions of population information displays, and 4) to explore the impact of population information display on cognitive outcomes. It concluded that a population database has great potential for reducing complexity and uncertainty in medicine to improve clinical care.
AHRQ-funded; HS023349.
Citation: Roosan D, Del Fiol G, Butler J .
Feasibility of population health analytics and data visualization for decision support in the infectious diseases domain: a pilot study.
Appl Clin Inform 2016 Jun 29;7(2):604-23. doi: 10.4338/aci-2015-12-ra-0182.
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Keywords: Clinical Decision Support (CDS), Data, Decision Making, Infectious Diseases, Public Health
Wang RC, Bent S, Weber E
The impact of clinical decision rules on computed tomography use and yield for pulmonary embolism: a systematic review and meta-analysis.
The researchers performed a systematic review of impact analyses on clinical decision rules for pulmonary embolism. They found that among participants with suspected pulmonary embolism, implementation of the Wells criteria was associated with a modest increase in CT angiography yield. They concluded that there is a lack of cluster-randomized trials to confirm the efficacy of clinical decision rules for the diagnosis of pulmonary embolism.
AHRQ-funded; HS021281.
Citation: Wang RC, Bent S, Weber E .
The impact of clinical decision rules on computed tomography use and yield for pulmonary embolism: a systematic review and meta-analysis.
Ann Emerg Med 2016 Jun;67(6):693-701.e3. doi: 10.1016/j.annemergmed.2015.11.005.
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Keywords: Clinical Decision Support (CDS), Decision Making, Imaging, Respiratory Conditions
Forster CS, Jerardi KE, Herbst L
Right test, wrong patient: biomarkers and value.
A 2-year-old girl with Pierre Robin sequence, a gastric tube, and a tracheostomy and ventilator was admitted to the hospital medicine service. The care delivered to this patient was not unsafe, and she did well. However, the value of care was almost certainly suboptimal. The continued emphasis on a single laboratory value (the procalcitonin test) rather than her clinical picture was the true driver behind the lower value of care delivered to this patient.
AHRQ-funded; HS023827.
Citation: Forster CS, Jerardi KE, Herbst L .
Right test, wrong patient: biomarkers and value.
Hosp Pediatr 2016 May;6(5):315-7. doi: 10.1542/hpeds.2015-0199.
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Keywords: Quality of Care, Children/Adolescents, Hospitalization, Clinical Decision Support (CDS), Healthcare Delivery
Tilson H, Hines LE, McEvoy G
AHRQ Author: Helwig AL
Recommendations for selecting drug-drug interactions for clinical decision support.
A work group consisting of 20 experts in pharmacology, drug information, and clinical decision support (CDS) from academia, government agencies, health information vendors, and healthcare organizations was convened. It recommended a transparent, systematic, and evidence-driven process with graded recommendations by a consensus panel of experts and oversight by a national organization.
AHRQ-authored.
Citation: Tilson H, Hines LE, McEvoy G .
Recommendations for selecting drug-drug interactions for clinical decision support.
Am J Health Syst Pharm 2016 Apr 15;73(8):576-85. doi: 10.2146/ajhp150565.
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Keywords: Clinical Decision Support (CDS), Adverse Drug Events (ADE), Medication: Safety, Medication, Health Information Technology (HIT)
Bauer NS, Carroll AE, Saha C
Experience with decision support system and comfort with topic predict clinicians' responses to alerts and reminders.
The researchers examined factors associated with clinician response to computer decision support system (CDSS) prompts as part of a larger, ongoing quality improvement effort to optimize CDSS use. They found that clinicians were more likely to respond to topics rated as "easy" to discuss. The position of the prompt on the page, clinician gender, and the patient's age, race/ethnicity, and preferred language were also predictive of prompt response rate.
AHRQ-funded; HS017939; HS020640; HS022681.
Citation: Bauer NS, Carroll AE, Saha C .
Experience with decision support system and comfort with topic predict clinicians' responses to alerts and reminders.
J Am Med Inform Assoc 2016 Apr;23(e1):e125-30. doi: 10.1093/jamia/ocv148.
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Keywords: Clinical Decision Support (CDS), Patient Safety, Children/Adolescents, Health Information Technology (HIT), Children/Adolescents
Taylor RA, Pare JR, Venkatesh AK
Prediction of in-hospital mortality in emergency department patients with sepsis: A local big data-driven, machine learning approach.
In this proof-of-concept study, a local, big data-driven, machine learning approach is compared to existing clinical decision rules (CDRs) and traditional analytic methods using the prediction of sepsis in-hospital mortality as the use case. It concluded that this approach outperformed existing CDRs as well as traditional analytic techniques for predicting in-hospital mortality of ED patients with sepsis.
AHRQ-funded; HS021271.
Citation: Taylor RA, Pare JR, Venkatesh AK .
Prediction of in-hospital mortality in emergency department patients with sepsis: A local big data-driven, machine learning approach.
Acad Emerg Med 2016 Mar;23(3):269-78. doi: 10.1111/acem.12876.
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Keywords: Emergency Medical Services (EMS), Mortality, Clinical Decision Support (CDS), Sepsis, Health Information Technology (HIT)
Khan S, McCullagh L, Press A
Formative assessment and design of a complex clinical decision support tool for pulmonary embolism.
This study sought to determine the general attitude towards clinical decision support (CDS) tool integration and the ideal integration point into the clinical workflow. It highlighted: (1) formative assessment of EHR functionality and clinical environment workflow, (2) focus groups and key informative interviews to incorporate providers' perceptions of CDS and workflow integration and/or (3) the demonstration of proposed workflows through wireframes to help providers visualise design concepts.
AHRQ-funded; HS022061.
Citation: Khan S, McCullagh L, Press A .
Formative assessment and design of a complex clinical decision support tool for pulmonary embolism.
Evid Based Med 2016 Feb;21(1):7-13. doi: 10.1136/ebmed-2015-110214.
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Keywords: Clinical Decision Support (CDS), Electronic Health Records (EHRs), Health Information Technology (HIT)
McCullagh LJ, Sofianou A, Kannry J
User centered clinical decision support tools: adoption across clinician training level.
This study examined the differences in adoption of CDS tools across providers’ training level. It found that the completion rates of the CDS calculator and medication order sets were higher among first year residents compared to all other training levels. Attending physicians were the less likely to accept the initial step of the CDS tool (29.3 percent) or complete the medication order sets (22.4 percent) that guided their prescription decisions.
AHRQ-funded; HS018491.
Citation: McCullagh LJ, Sofianou A, Kannry J .
User centered clinical decision support tools: adoption across clinician training level.
Appl Clin Inform 2014 Dec 17;5(4):1015-25. doi: 10.4338/aci-2014-05-ra-0048.
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Keywords: Clinical Decision Support (CDS), Decision Making, Practice Patterns
Einbinder J, Hebel E, Wright A
The number needed to remind: a measure for assessing CDS effectiveness.
The purpose of this paper is to provide a better understanding of population based clinical decision support (CDS) performance measurement, to identify best practices for designing and implementing CDS, and to introduce two new quality measures, titled Reminder Performance (RP) and the Number Needed to Remind (NNR) for evaluating the effectiveness of clinical reminders in the context of the CDS Dashboards.
AHRQ-funded; 290200810010.
Citation: Einbinder J, Hebel E, Wright A .
The number needed to remind: a measure for assessing CDS effectiveness.
AMIA Annu Symp Proc 2014 Nov 14;2014:506-15..
Keywords: Decision Making, Clinical Decision Support (CDS), Quality Measures, Quality of Care