The Use of Evidence-Based, Problem-Oriented Templates as a Clinical Decision Support in an Inpatient Electronic Health Record System.
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SUBMITTER: Mehta R
PROVIDER: S-EPMC5052550 | biostudies-other | 2016 Aug
REPOSITORIES: biostudies-other
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Project description:BACKGROUND:Smokers usually abstain from tobacco while hospitalized but relapse after discharge. Inpatient interventions may encourage sustained quitting. We previously demonstrated that a decision support tool embedded in an electronic health record (EHR) improved physicians' treatment of hospitalized smokers. This report describes the effect on quit rates of this decision support tool and order set for hospitalized smokers. METHODS:In a single hospital system, 254 physicians were randomized 1:1 to receive a decision support tool and order set, embedded in the EHR. When an adult patient was admitted to a medical service, an electronic alert appeared if current smoking was recorded in the EHR. For physicians receiving the intervention, the alert linked to an order set for tobacco treatment medications and electronic referral to the state tobacco quitline. Additionally, "Tobacco Use Disorder" was added to the patient's problem list, and a secure message was sent to the patient's primary care provider (PCP). In the control arm, no alert appeared. Patients were contacted by phone at 1, 6, and 12?months; those reporting tobacco abstinence at 12?months were asked to return to measure exhaled carbon monoxide. Generalized estimating equations were used to model the data. RESULTS:From 2013 to 2016, the alert fired for 10,939 patients (5391 intervention, 5548 control). Compared to control physicians, intervention physicians were more likely to order tobacco treatment medication, populate the problem list with tobacco use disorder, refer to the quitline, and notify the patient's PCP. In a subset of 1044 patients recruited for intensive follow-up, one-year quit rates for intervention and control patients were, respectively, 11.5% and 11.6%, (p?=?0.94), after controlling for age, sex, race, ethnicity, and insurance. Similarly, there were no differences in 1- and 6-month quit rates. CONCLUSIONS:Although we were able to improve processes of care, long-term tobacco quit rates were unchanged. This likely reflects, in part, the need for sustained quitting interventions, and higher-than-expected quit rates in controls. Future enhancements should improve prescription of medications for smoking cessation at discharge, engagement of primary care providers, and perhaps direct engagement of patients in a more longitudinal approach. TRIAL REGISTRATION:ClinicalTrials.gov, NCT01691105 . Registered on September 12, 2012.
| S-EPMC6343239 | biostudies-literature
Project description:Tobacco dependence treatment for hospitalized smokers results in long-term cessation if treatment continues at least 30 days post-discharge. Health information technology may facilitate ongoing tobacco dependence treatment after hospital discharge. To describe the use and impact of a new decision support tool and order set for inpatient physicians, addressing tobacco dependence treatment for hospitalized smokers, embedded in an electronic health record (EHR). In a cluster-randomized trial, 254 physicians were randomized (1:1) to either receive or not receive the decision support tool and order set, which were embedded in the Epic (Madison, WI) EHR used at 2 hospitals in a single city. When an adult patient was admitted to a medical service, an electronic alert appeared if the patient was coded in the EHR as a smoker. For physicians randomized to the intervention, the alert linked to an order set to prescribe tobacco treatment medications and refer the patient to the state tobacco quitline. Additionally, "tobacco use disorder" was added to the patient's problem list, and an e-mail was sent to the patient's primary care provider (PCP). In the control arm, an alert fired with no screen visibility. Generalized estimating equations were used to model the data. Since August 2013, the alert has appeared for 10,939 patients (5391 intervention, 5548 control). Compared to control physicians, intervention physicians were more likely to order tobacco treatment medication (35 vs. 29%, P < 0.0001), populate the problem list with tobacco use disorder (41 vs. 2%, P < 0.0001), and make a referral to the state smokers' quitline (30 vs. 0%, P < 0.0001). In addition, intervention physicians sent an e-mail to the patient's PCP 4152 (99%) times. Designing and implementing an order set and alert for tobacco treatment in an EHR is feasible and helps physicians place more orders for tobacco treatment medication, referrals to the state smokers' quitline, and e-mails to patients' PCPs. Data on cessation outcomes are pending.www.ClinicalTrials.gov (NCT01691105).
| S-EPMC5526813 | biostudies-literature
Project description:BackgroundPhysiologic neonatal hyperbilirubinemia (jaundice) is common and affects most newborn infants. However, there is a risk for permanent neurological damage if the bilirubin levels rise above a certain threshold. The management of neonatal jaundice includes the assessment of bilirubin laboratory values, consideration of patient-specific risk factors, and plotting on a bilirubin nomogram reference to determine risk and guide therapy. When performed manually, the process can be time consuming and error-prone. Therefore, web-based calculators such as BiliTool have been developed to assist in risk assessment.MethodsTo streamline the risk assessment calculation process further within our electronic health record (EHR), we created a "BiliReport" to display patient bilirubin-related data and automate transmission of deidentified patient data to the BiliTool website (https://bilitool.org). After implementation, we evaluated usage data, provider satisfaction, and accuracy of documentation.ResultsWe demonstrated high provider use of the BiliReport and satisfaction with the workflow. We found a significant improvement in the accuracy of bilirubin risk level documentation, with a reduction in erroneous risk stratification from 4% (15/232) to 0.4% (1/243), P < 0.001. We did not find significant a difference in erroneous documentation of the bilirubin lab value (P = 0.07).ConclusionsIntegrating the neonatal hyperbilirubinemia risk assessment process into the EHR may reduce errors and improve provider documentation and adherence to recommended guidelines.
| S-EPMC8221402 | biostudies-literature
Project description:Background and objectiveMedical groups have invested billions of dollars in electronic medical records (EMRs), but few studies have examined the cost-effectiveness of EMR-based clinical decision support (CDS). This study examined the cost-effectiveness of EMR-based CDS for adults with diabetes from the perspective of the health care system.Data sources/settingClinical outcome and cost data from a randomized clinical trial of EMR-based CDS were used as inputs into a diabetes simulation model. The simulation cohort included 1,092 patients with diabetes with A1c above goal at baseline.Study designThe United Kingdom Prospective Diabetes Study Outcomes Model, a validated simulation model of diabetes, was used to evaluate remaining life years, quality-adjusted life years (QALYs), and health care costs over patient lifetimes (40-year time horizon) from the health system perspective.Principal findingsPatients in the intervention group had significantly lowered A1c (0.26 percent, p = .014) relative to patients in the control arm. Intervention costs were $120 (SE = 45) per patient in the first year and $76 (SE = 45) per patient in the following years. In the base case analysis, EMR-based CDS increased lifetime QALYs by 0.04 (SE = 0.01) and increased lifetime costs by $112 (SE = 660), resulting in an incremental cost-effectiveness ratio of $3,017 per QALY. The cost-effectiveness of EMR-based CDS persisted in one-way, two-way, and probabilistic sensitivity analyses.ConclusionsWidespread adoption of sophisticated EMR-based CDS has the potential to modestly improve the quality of care for patients with chronic conditions without substantially increasing costs to the health care system.
| S-EPMC3459233 | biostudies-literature
Project description:ObjectiveAvailable studies on implementation of evidence-based psychotherapy (EBP) for patients attending Department of Veterans Affairs (VA) residential post-traumatic stress disorder (PTSD) programs rely on therapist self-report of EBP delivery. Patient-level data on receipt of EBP are needed both to corroborate therapist self-report and to understand patient factors that predict receipt of EBPs for PTSD.Materials and methodsWe identified 159 therapists from 38 VA residential PTSD programs who responded to a survey about EBP implementation during the 2015 fiscal year (FY15). Therapists self-reported their use of two EBPs, including prolonged exposure delivered in an individual format (PE-I) and cognitive processing therapy delivered in individual and group formats (CPT-I and CPT-G). Using electronic medical record (EMR) templates mandated for EBP documentation in FY15, we measured contemporaneous patient-level receipt of EBPs for PTSD. We assessed the degree of correlation between therapist self-reported EBP delivery and patient receipt of EBT as measured by EMR templates using polychoric correlation coefficients. We determined patient and therapist factors that predicted the receipt of EBPs with multivariable logistic regression, using random effects and robust standard error estimation, and controlling for site. The Veterans IRB of Northern New England provided a waiver of informed consent; as this was a retrospective review, no patients or therapists were contacted, and all data were stored, transmitted, and analyzed on secure VA servers. The VA Connecticut Health Care System Human Research Protection Program approved secondary use of therapist survey data for this project.ResultsWhen EMR template use became mandated in FY15, the proportion of patients in residential PTSD programs who received at least one EBP session that was recorded with an EMR template increased dramatically from 8.8% to 33.9%. There was adequate correlation and between survey-based and EMR-based measures of EBP receipt, with polychoric correlation values of 0.77 for PE-I, 0.69 for CPT-I, and 0.82 for CPT-G. Multiple patient factors were positive (e.g., female gender) and negative (e.g., depressive disorders) predictors of receipt of EBPs, even after controlling for site. Among therapist factors, only EBP consultant or trainer status was a positive predictor of EBP provision and only therapist race was a negative predictor of EBT provision after controlling for site.ConclusionFollowing a FY15 mandate, EMR templates documenting EBP delivery were widely used by therapists working in VA residential PTSD programs. EBP receipt measured using EMR templates was consistent with therapist self-report of EBT delivery. There were several patient-level predictors of EBP receipt and therapist-level predictors of EBP delivery. However, therapists most likely to deliver EBPs were clustered at a limited number of sites.
| S-EPMC6115864 | biostudies-literature
Project description:Morphine is the opioid most commonly used for neonatal pain management. In intravenous form, it is administered as continuous infusions and intermittent injections, mostly based on empirically established protocols. Inadequate pain control in neonates can cause long-term adverse consequences; however, providing appropriate individualized morphine dosing is particularly challenging due to the interplay of rapid natural physiological changes and multiple life-sustaining procedures in patients who cannot describe their symptoms. At most institutions, morphine dosing in neonates is largely carried out as an iterative process using a wide range of starting doses and then titrating to effect based on clinical response and side effects using pain scores and levels of sedation. Our background data show that neonates exhibit large variability in morphine clearance resulting in a wide range of exposures, which are poorly predicted by dose alone. Here, we describe the development and implementation of an electronic health record-integrated, model-informed decision support platform for the precision dosing of morphine in the management of neonatal pain. The platform supports pharmacokinetic model-informed dosing guidance and has functionality to incorporate real-time drug concentration information. The feedback is inserted directly into prescribers' workflows so that they can make data-informed decisions. The expected outcomes are better clinical efficacy and safety with fewer side effects in the neonatal population.
| S-EPMC7378965 | biostudies-literature
Project description:ObjectiveSubstantial investment in electronic health records (EHRs) has provided an unprecedented opportunity to use clinical decision support (CDS) to increase guideline adherence. To inform efforts to maximize adoption, we characterized the adoption of an otitis media (OM) CDS system, the impact of performance feedback on adoption, and the effects of adoption on guideline adherence.Study settingA total of 41,391 OM visits with 108 clinicians at 16 pediatric practices between February 2009 and August 2010.Study designProspective cohort study of EHR-based CDS adoption during OM visits, comparing clinicians receiving performance feedback to none. CDS was available to all physicians; use was voluntary.Data collectionExtraction from a common EHR.Principal findingsClinicians and practices used the CDS system for a mean of 21 percent (range: 0-85 percent) and 17 percent (0-51 percent) of eligible OM visits, respectively. Clinicians who received performance feedback reports summarizing CDS use and guideline adherence had a relative increase in CDS use of 9.0 percentage points compared to others (p = .001). CDS adoption was associated with increased OM guideline adherence. Effects were greatest among clinicians with the lowest adherence prior to the study.ConclusionsPerformance feedback increased CDS adoption, but additional strategies are needed to integrate CDS into primary care workflows.
| S-EPMC4369219 | biostudies-literature
Project description:Accurate clinical problem lists are critical for patient care, clinical decision support, population reporting, quality improvement, and research. However, problem lists are often incomplete or out of date.To determine whether a clinical alerting system, which uses inference rules to notify providers of undocumented problems, improves problem list documentation.Inference rules for 17 conditions were constructed and an electronic health record-based intervention was evaluated to improve problem documentation. A cluster randomized trial was conducted of 11 participating clinics affiliated with a large academic medical center, totaling 28 primary care clinical areas, with 14 receiving the intervention and 14 as controls. The intervention was a clinical alert directed to the provider that suggested adding a problem to the electronic problem list based on inference rules. The primary outcome measure was acceptance of the alert. The number of study problems added in each arm as a pre-specified secondary outcome was also assessed. Data were collected during 6-month pre-intervention (11/2009-5/2010) and intervention (5/2010-11/2010) periods.17,043 alerts were presented, of which 41.1% were accepted. In the intervention arm, providers documented significantly more study problems (adjusted OR=3.4, p<0.001), with an absolute difference of 6277 additional problems. In the intervention group, 70.4% of all study problems were added via the problem list alerts. Significant increases in problem notation were observed for 13 of 17 conditions.Problem inference alerts significantly increase notation of important patient problems in primary care, which in turn has the potential to facilitate quality improvement.ClinicalTrials.gov: NCT01105923.
| S-EPMC3384110 | biostudies-literature
Project description:We wanted to assess the impact of an electronic health record-based diabetes clinical decision support system on control of hemoglobin A(1c) (glycated hemoglobin), blood pressure, and low-density lipoprotein (LDL) cholesterol levels in adults with diabetes.We conducted a clinic-randomized trial conducted from October 2006 to May 2007 in Minnesota. Included were 11 clinics with 41 consenting primary care physicians and the physicians' 2,556 patients with diabetes. Patients were randomized either to receive or not to receive an electronic health record (EHR)-based clinical decision support system designed to improve care for those patients whose hemoglobin A(1c), blood pressure, or LDL cholesterol levels were higher than goal at any office visit. Analysis used general and generalized linear mixed models with repeated time measurements to accommodate the nested data structure.The intervention group physicians used the EHR-based decision support system at 62.6% of all office visits made by adults with diabetes. The intervention group diabetes patients had significantly better hemoglobin A(1c) (intervention effect -0.26%; 95% confidence interval, -0.06% to -0.47%; P=.01), and better maintenance of systolic blood pressure control (80.2% vs 75.1%, P=.03) and borderline better maintenance of diastolic blood pressure control (85.6% vs 81.7%, P =.07), but not improved low-density lipoprotein cholesterol levels (P = .62) than patients of physicians randomized to the control arm of the study. Among intervention group physicians, 94% were satisfied or very satisfied with the intervention, and moderate use of the support system persisted for more than 1 year after feedback and incentives to encourage its use were discontinued.EHR-based diabetes clinical decision support significantly improved glucose control and some aspects of blood pressure control in adults with type 2 diabetes.
| S-EPMC3022040 | biostudies-literature
Project description:BackgroundSepsis remains the top cause of morbidity and mortality of hospitalised patients despite concerted efforts. Clinical decision support for sepsis has shown mixed results reflecting heterogeneous populations, methodologies and interventions.ObjectivesTo determine whether the addition of a real-time electronic health record (EHR)-based clinical decision support alert improves adherence to treatment guidelines and clinical outcomes in hospitalised patients with suspected severe sepsis.DesignPatient-level randomisation, single blinded.SettingMedical and surgical inpatient units of an academic, tertiary care medical centre.Patients1123 adults over the age of 18 admitted to inpatient wards (intensive care units (ICU) excluded) at an academic teaching hospital between November 2014 and March 2015.InterventionsPatients were randomised to either usual care or the addition of an EHR-generated alert in response to a set of modified severe sepsis criteria that included vital signs, laboratory values and physician orders.Measurements and main resultsThere was no significant difference between the intervention and control groups in primary outcome of the percentage of patients with new antibiotic orders at 3 hours after the alert (35% vs 37%, p=0.53). There was no difference in secondary outcomes of in-hospital mortality at 30 days, length of stay greater than 72 hours, rate of transfer to ICU within 48 hours of alert, or proportion of patients receiving at least 30 mL/kg of intravenous fluids.ConclusionsAn EHR-based severe sepsis alert did not result in a statistically significant improvement in several sepsis treatment performance measures.
| S-EPMC6860967 | biostudies-literature