National Healthcare Quality and Disparities Report
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- Adverse Drug Events (ADE) (5)
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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 59 Research Studies DisplayedPress 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)
Goldstein SL
Automated/integrated real-time clinical decision support in acute kidney injury.
The author argues that early, real-time identification and notification to healthcare providers of patients at risk for, or with, acute or chronic kidney disease can drive simple interventions to reduce harm. Similarly, he believes that screening patients at risk for acute kidney injury with these platforms to alert research personnel will lead to improve study subject recruitment.
AHRQ-funded; HS023763; HS021114.
Citation: Goldstein SL .
Automated/integrated real-time clinical decision support in acute kidney injury.
Curr Opin Crit Care 2015 Dec;21(6):485-9. doi: 10.1097/mcc.0000000000000250.
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Keywords: Clinical Decision Support (CDS), Kidney Disease and Health, Electronic Health Records (EHRs), Patient-Centered Outcomes Research, Diagnostic Safety and Quality
Almario CV, Chey WD, Iriana S
Computer versus physician identification of gastrointestinal alarm features.
This study's objective was to compare the number of alarms documented by physicians during usual care vs. that collected by a computer algorithm called Automated Evaluation of Gastrointestinal Symptoms (AEGIS). AEGIS identified more patients with positive alarm features compared to physicians and also documented more positive alarms. Moreover, clinicians documented only 30% of the positive alarms self-reported by patients through AEGIS.
AHRQ-funded; HS000046.
Citation: Almario CV, Chey WD, Iriana S .
Computer versus physician identification of gastrointestinal alarm features.
Int J Med Inform 2015 Dec;84(12):1111-7. doi: 10.1016/j.ijmedinf.2015.07.006.
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Keywords: Clinical Decision Support (CDS), Diagnostic Safety and Quality, Digestive Disease and Health, Electronic Health Records (EHRs), Patient Safety
Liang C, Gong Y
Enhancing patient safety event reporting by K-nearest neighbor classifier.
The debate on structured or unstructured data entry reveals not only a trade-off problem among data accuracy, completeness, and timeliness, but also a technical gap on text mining. The reesarchers suggested a text classification method for predicting subject categories. Their results demonstrated the feasibility of their system and indicated the advantage of such an application to raise data quality and clinical decision support in reporting patient safety events.
AHRQ-funded; HS022895.
Citation: Liang C, Gong Y .
Enhancing patient safety event reporting by K-nearest neighbor classifier.
Stud Health Technol Inform 2015;218:40603.
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Keywords: Adverse Events, Medical Errors, Patient Safety, Public Reporting, Clinical Decision Support (CDS), Health Information Technology (HIT), Data
Lo Re V, 3rd, Haynes K, Forde KA
Risk of acute liver failure in patients with drug-induced liver injury: evaluation of Hy's Law and a new prognostic model.
The researchers aimed to develop a highly sensitive model to identify drug-induced liver injury (DILI) patients at increased risk of acute liver failure (ALF). negative predictive value (0.99), but low level of sensitivity (0.68) and positive predictive value (0.02). Their model, comprising data on platelet count and total bilirubin level, identified patients with ALF with a C statistic of 0.87 and enabled calculation of a risk score (Drug-Induced Liver Toxicity ALF Score).
AHRQ-funded; HS018372.
Citation: Lo Re V, 3rd, Haynes K, Forde KA .
Risk of acute liver failure in patients with drug-induced liver injury: evaluation of Hy's Law and a new prognostic model.
Clin Gastroenterol Hepatol 2015 Dec;13(13):2360-8. doi: 10.1016/j.cgh.2015.06.020.
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Keywords: Antimicrobial Stewardship, Medication, Chronic Conditions, Adverse Drug Events (ADE), Clinical Decision Support (CDS)
Panahiazar M, Taslimitehrani V, Pereira NL
Using EHRs for heart failure therapy recommendation using multidimensional patient similarity analytics.
The authors developed a multidimensional patient similarity assessment technique that leverages multiple types of information from the electronic health records and predicts a medication plan for each new patient based on prior knowledge and data from similar patients.Their findings suggest that it is feasible to harness population-based information for an individual patient-specific assessment.
AHRQ-funded; HS023077.
Citation: Panahiazar M, Taslimitehrani V, Pereira NL .
Using EHRs for heart failure therapy recommendation using multidimensional patient similarity analytics.
Stud Health Technol Inform 2015;210:369-73.
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Keywords: Clinical Decision Support (CDS), Data, Electronic Health Records (EHRs), Heart Disease and Health, Patient-Centered Healthcare
Islam R, Weir CR, Jones M
Understanding complex clinical reasoning in infectious diseases for improving clinical decision support design.
The purpose of the study was to examine the constituents of decision complexity and explore the cognitive strategies clinicians use to control and adapt to their information environment. The following three themes emerged as the constituents of decision complexity experienced by the Infectious Diseases experts: 1) the overall clinical picture does not match the pattern, 2) a lack of comprehension of the situation and 3) dealing with social and emotional pressures such as fear and anxiety.
AHRQ-funded; HS023349.
Citation: Islam R, Weir CR, Jones M .
Understanding complex clinical reasoning in infectious diseases for improving clinical decision support design.
BMC Med Inform Decis Mak 2015 Nov 30;15:101. doi: 10.1186/s12911-015-0221-z.
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Keywords: Clinical Decision Support (CDS), Health Services Research (HSR), Practice Patterns
Wright A, Sittig DF, Ash JS
Lessons learned from implementing service-oriented clinical decision support at four sites: a qualitative study.
This study identified challenges, lessons learned and best practices for service-oriented clinical decision support, based on the results of the Clinical Decision Support Consortium, a multi-site study which developed, implemented and evaluated clinical decision support services in a diverse range of electronic health records. Based on the challenges and lessons learned, there were eight best practices for developers and implementers of service-oriented clinical decision support.
AHRQ-funded; 290200810010.
Citation: Wright A, Sittig DF, Ash JS .
Lessons learned from implementing service-oriented clinical decision support at four sites: a qualitative study.
Int J Med Inform 2015 Nov;84(11):901-11. doi: 10.1016/j.ijmedinf.2015.08.008.
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Keywords: Clinical Decision Support (CDS), Electronic Health Records (EHRs), Decision Making, Health Information Technology (HIT)
Gephart S, Carrington JM, Finley B
A systematic review of nurses' experiences with unintended consequences when using the electronic health record.
The purpose of this article is to present the state of the science on nurses' experiences with unintended consequences of electronic health records (EHRs). Findings demonstrate that nurses experience changes to workflow, must continually adapt to meet patient's needs in the context of imperfect EHR systems, and have difficulty accessing the information they need to make patient care decisions. Implications for nurse administrators include the need for continual engagement with nurses along the continuum of EHR design, as well as the need to encourage nurses to speak up and acknowledge workflow changes that threaten patient safety or do not support work efficiency.
AHRQ-funded; HS021074.
Citation: Gephart S, Carrington JM, Finley B .
A systematic review of nurses' experiences with unintended consequences when using the electronic health record.
Nurs Adm Q 2015 Oct-Dec;39(4):345-56. doi: 10.1097/naq.0000000000000119.
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Keywords: Adverse Events, Clinical Decision Support (CDS), Electronic Health Records (EHRs), Nursing, Workflow
Fumo DE, Kapoor V, Reece LJ
Historical matching strategies in kidney paired donation: the 7-year evolution of a web-based virtual matching system.
Failure to convert computer-identified possible kidney paired donation (KPD) exchanges into transplants has prohibited KPD from reaching its full potential. This study analyzes the progress of exchanges in moving from "offers" to completed transplants. The "offer" and 1-way success rates were 21.9 and 15.5 percent, respectively. Three reasons for failure were found that could be prospectively prevented by changes in protocol or software.
AHRQ-funded; HS020610.
Citation: Fumo DE, Kapoor V, Reece LJ .
Historical matching strategies in kidney paired donation: the 7-year evolution of a web-based virtual matching system.
Am J Transplant 2015 Oct;15(10):2646-54. doi: 10.1111/ajt.13337.
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Keywords: Health Information Technology (HIT), Transplantation, Decision Making, Clinical Decision Support (CDS)
Bray M, Wang W, Song PX
Planning for uncertainty and fallbacks can increase the number of transplants in a kidney-paired donation program.
The researchers outlined and examined, through example and by simulation, four schemes for selecting potential matches in a realistic model of a kidney-paired donation system. Their proposed schemes take account of probabilities that chosen transplants may not be completed as well as allowing for contingency plans when the optimal solution fails.
AHRQ-funded; HS020610.
Citation: Bray M, Wang W, Song PX .
Planning for uncertainty and fallbacks can increase the number of transplants in a kidney-paired donation program.
Am J Transplant 2015 Oct;15(10):2636-45. doi: 10.1111/ajt.13413.
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Keywords: Transplantation, Clinical Decision Support (CDS), Health Information Technology (HIT)
Dugan TM, Mukhopadhyay S, Carroll A
Machine learning techniques for prediction of early childhood obesity.
This study aimed to predict childhood obesity after age two, using only data collected prior to the second birthday by a clinical decision support system called CHICA. It demonstrated that data from a production clinical decision support system can be used to build an accurate machine learning model to predict obesity in children after age two.
AHRQ-funded; HS020640; HS018453; HS017939.
Citation: Dugan TM, Mukhopadhyay S, Carroll A .
Machine learning techniques for prediction of early childhood obesity.
Appl Clin Inform 2015 Aug 12;6(3):506-20. doi: 10.4338/aci-2015-03-ra-0036.
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Keywords: Children/Adolescents, Obesity, Health Information Technology (HIT), Clinical Decision Support (CDS), Children/Adolescents