The Impact of an Electronic Health Record Intervention on Spirometry Completion in Patients with Chronic Obstructive Pulmonary Disease.

Spirometry is necessary to diagnose chronic obstructive pulmonary disease (COPD), yet a large proportion of patients are diagnosed and treated without having received testing. This study explored whether the effects of interventions using the electronic health record (EHR) to target patients diagnosed with COPD without confirmatory spirometry impacted the incidence rates of spirometry referrals and completions. This retrospective before and after study assessed the impact of provider-facing clinical decision support that identified patients who had a diagnosis of COPD but had not received spirometry. Spirometry referrals, completions, and results were ascertained 1.5 years prior to and 1.5 years after the interventions were initiated. Inhaler prescriptions by class were also tallied. There were 10,949 unique patients with a diagnosis of COPD who were eligible for inclusion. 4,895 patients (44.7%) were excluded because they had completed spirometry prior to the cohort start dates. The pre-intervention cohort consisted of 2,622 patients, while the post-intervention cohort had 3,392. Spirometry referral rates pre-intervention were 20.2% compared to 31.6% post-intervention (p < 0.001). Spirometry completion rates rose from 13.2% pre-intervention to 19.3% afterwards (p < 0.001). 61.7% (585 of 948) had no evidence of airflow obstruction. After excluding patients with a diagnosis of asthma, 25.8% (126 of 488) patients who had no evidence of airflow obstruction had prescriptions for long-acting bronchodilators or inhaled steroids. A concerted EHR intervention modestly increased spirometry referral and completion rates in patients with a diagnosis of COPD without prior spirometry and decreased misclassification of disease.

Assessing Different Approaches to Leveraging Historical Smoking Exposure Data to Better Select Lung Cancer Screening Candidates: A Retrospective Validation Study.

INTRODUCTION: There is mounting interest in the use of risk prediction models to guide lung cancer screening. Electronic health records (EHRs) could facilitate such an approach, but smoking exposure documentation is notoriously inaccurate. While the negative impact of inaccurate EHR data on screening practices reliant on dichotomized age and smoking exposure-based criteria has been demonstrated, less is known regarding its impact on the performance of model-based screening. AIMS AND METHODS: Data were collected from a cohort of 37 422 ever-smokers between the ages of 55 and 74, seen at an academic safety-net healthcare system between 1999 and 2018. The National Lung Cancer Screening Trial (NLST) criteria, PLCOM2012 and LCRAT lung cancer risk prediction models were validated against time to lung cancer diagnosis. Discrimination (area under the receiver operator curve [AUC]) and calibration were assessed. The effect of substituting the last documented smoking variables with differentially retrieved "history conscious" measures was also determined. RESULTS: The PLCOM2012 and LCRAT models had AUCs of 0.71 (95% CI, 0.69 to 0.73) and 0.72 (95% CI, 0.70 to 0.74), respectively. Compared with the NLST criteria, PLCOM2012 had a significantly greater time-dependent sensitivity (69.9% vs. 64.5%, p < .01) and specificity (58.3% vs. 56.4%, p < .001). Unlike the NLST criteria, the performances of the PLCOM2012 and LCRAT models were not prone to historical variability in smoking exposure documentation. CONCLUSIONS: Despite the inaccuracies of EHR-documented smoking histories, leveraging model-based lung cancer risk estimation may be a reasonable strategy for screening, and is of greater value compared with using NLST criteria in the same setting. IMPLICATIONS: EHRs are potentially well suited to aid in the risk-based selection of lung cancer screening candidates, but healthcare providers and systems may elect not to leverage EHR data due to prior work that has shown limitations in structured smoking exposure data quality. Our findings suggest that despite potential inaccuracies in the underlying EHR data, screening approaches that use multivariable models may perform significantly better than approaches that rely on simpler age and exposure-based criteria. These results should encourage providers to consider using pre-existing smoking exposure data with a model-based approach to guide lung cancer screening practices.

Using Preventive Health Alerts in the Electronic Health Record Improves Hepatitis C Virus Testing Among Infants Perinatally Exposed to Hepatitis C.

BACKGROUND: Perinatal exposure to hepatitis C virus (HCV) is a major public health issue, and poor testing rates leave many children with infection unidentified. We sought to use the electronic health record (EHR) to promote guideline-directed HCV testing among infants born to mothers with HCV infection in an urban, safety-net hospital system. METHODS: Our study population was identified using our EHR database, Epic. Children were included in the study if they had perinatal HCV exposure, were 18 months to 18 years of age and had at least 1 encounter in a primary or urgent care clinic during the study period. Our study included retrospective (October 2011 to February 2015) and prospective (February 2015 to May 2018) arms. Our EHR-based intervention was initiated in the prospective arm and recommended a one-time HCV antibody test at or after the age of 18 months using a health maintenance reminder. The health maintenance reminder activated a point-of-care alert and a linked HCV testing order set in all prespecified encounters during the intervention period. RESULTS: Median time to appropriate HCV testing decreased from 96.2 months preintervention to 9.1 months postintervention (P < 0.0001), and rate of completed antibody testing increased from 14% to 61% (P < 0.0001). CONCLUSIONS: Among children with perinatal HCV exposure, using a point-of-care alert within the EHR significantly increased the HCV antibody testing rate in accordance with American Academy of Pediatrics (AAP) recommendations. More effective EHR-based interventions combined with increased provider awareness of appropriate HCV testing in perinatally exposed infants is imperative.

Doubling Hepatitis C Virus Screening in Primary Care Using Advanced Electronic Health Record Tools-A Non-Randomized Controlled Trial.

BACKGROUND: Hepatitis C virus (HCV) infection is a major public health burden, affecting over 4 million people. The Centers for Disease Control and Prevention and the US Preventive Services Task Force guidelines recommend screening everyone born between 1945 and 1965, but screening rates remain low. OBJECTIVE: To determine whether bulk ordering and electronic messaging to patients improves guideline-based HCV screening rates. DESIGN: A non-randomized controlled trial of 1024 adults from November 2016 to March 2017. PARTICIPANTS: Patients due for HCV screening with at least one primary care office visit in one of three primary care clinics and enrolled in the healthcare system's tethered personal health record (tPHR). INTERVENTIONS: Control patients received normal care for HCV screening, consisting of passive HCV reminders to providers during face-to-face visits and passive HCV screening notification through the patient's tPHR. Intervention patients received normal care and also had HCV antibody tests ordered for them and customized messages sent through their tPHR inviting them to go directly to the lab for HCV screening over a 12-week period. MAIN MEASURES: Percentage/number of patients receiving HCV antibody tests during the intervention period. Percentage/number of intervention group patients receiving HCV screening with other blood work. KEY RESULTS: In the intervention group, 33% (168 of 514) completed HCV testing, compared with 19% (97 of 510) of controls (OR 1.7, 95% CI 1.2-2.1). Bulk lab ordering appeared to have a large impact while bulk messaging appeared to have a less significant role. CONCLUSIONS: Leveraging population analytics and bulk ordering in an electronic health record with bulk messaging to a tPHR directly engages patients in blood screening tests and can significantly improve completion. This methodology has a broad range of applications including many recommended screening or disease-specific testing. This bulk ordering and direct-to-patient messaging approach improves patient screening while decreasing provider/staff work. TRIAL REGISTRATION: MetroHealth IRB16-00776 (ClinicalTrials.gov).

Optimizing drug-dose alerts using commercial software throughout an integrated health care system.

All default electronic health record and drug reference database vendor drug-dose alerting recommendations (single dose, daily dose, dose frequency, and dose duration) were silently turned on in inpatient, outpatient, and emergency department areas for pediatric-only and nonpediatric-only populations. Drug-dose alerts were evaluated during a 3-month period. Drug-dose alerts fired on 12% of orders (104 098/834 911). System-level and drug-specific strategies to decrease drug-dose alerts were analyzed. System-level strategies included: (1) turning off all minimum drug-dosing alerts, (2) turning off all incomplete information drug-dosing alerts, (3) increasing the maximum single-dose drug-dose alert threshold to 125%, (4) increasing the daily dose maximum drug-dose alert threshold to 125%, and (5) increasing the dose frequency drug-dose alert threshold to more than 2 doses per day above initial threshold. Drug-specific strategies included changing drug-specific maximum single and maximum daily drug-dose alerting parameters for the top 22 drug categories by alert frequency. System-level approaches decreased alerting to 5% (46 988/834 911) and drug-specific approaches decreased alerts to 3% (25 455/834 911). Drug-dose alerts varied between care settings and patient populations.

Code Status Reconciliation to Improve Identification and Documentation of Code Status in Electronic Health Records.

BACKGROUND: Code status (CS) of a patient (part of their end-of-life wishes) can be critical information in healthcare delivery, which can change over time, especially at transitions of care. Although electronic health record (EHR) tools exist for medication reconciliation across transitions of care, much less attention is given to CS, and standard EHR tools have not been implemented for CS reconciliation (CSR). Lack of CSR creates significant potential patient safety and quality of life issues. OBJECTIVE: To study the tools, workflow, and impact of clinical decision support (CDS) for CSR. METHODS: We established rules for CS implementation in our EHR. At admission, a CS is required as part of a patient's admission order set. Using standard CDS tools in our EHR, we built an interruptive alert for CSR at discharge if a patient did not have the same inpatient (current) CS at discharge as that prior to admission CS. RESULTS: Of 80,587 admissions over a four year period (2 years prior to and post CSR implementation), CS discordance was seen in 3.5% of encounters which had full code status prior to admission, but Do Not Resuscitate (DNR) CS at discharge. In addition, 1.4% of the encounters had a different variant of the DNR CS at discharge when compared with CS prior to admission. On pre-post CSR implementation analysis, DNR CS per 1000 admissions per month increased significantly among patients discharged and in patients being admitted (mean ± SD: 85.36 ± 13.69 to 399.85 ± 182.86, p<0.001; and 1.99 ± 1.37 vs 16.70 ± 4.51, p<0.001, respectively). CONCLUSION: EHR enabled CSR is effective and represents a significant informatics opportunity to help honor patients' end-of-life wishes. CSR represents one example of non-medication reconciliation at transitions of care that should be considered in all EHRs to improve care quality and patient safety.

When is family history obtained? - Lack of timely documentation of family history among overweight and hypertensive paediatric patients.

AIM: Taking a detailed family history is an inexpensive way for healthcare providers to screen patients for increased risk of various chronic conditions. Documentation of family history, however, has been shown to be incomplete in the majority of patient charts. The current study examines when family history is collected within the context of the development and diagnosis of chronic conditions in paediatrics, using hypertension and overweight/obesity as examples. METHODS: We analysed family history data from the electronic medical records of 5485 overweight/obese and 774 hypertensive children and adolescents in a large, urban medical system in northeast Ohio. Manual review of 200 charts was also performed. RESULTS: Family history information was entered prior to the development of hypertension in 13.5% of hypertensive patients with a family history of hypertension, and it was entered prior to the development of abnormal weight in 35.5% of overweight/obese patients with a family history of obesity or a related condition. Of patients with a relevant family history who received an actual diagnosis for either of these conditions, only 16.7% of hypertensive and 33.3% of overweight/obese patients had this family history documented prior to diagnosis. CONCLUSIONS: These results imply that paediatric providers may not use family history as a screening tool for assessing future risk of obesity and hypertension, but instead gather this information after these chronic conditions have developed, making it difficult to implement preventative or screening strategies based on familial risk.

The trivialization of diagnosis.

Although it is widely recognized that diagnosis plays a central role in clinical medicine, in recent years the primacy of diagnosis has come under attack from several sources. 1. "Billable terms" are replacing traditional medical diagnoses. The former are based on International Classification of Diseases lists, which include many non-diagnoses such as symptoms and signs. 2. Diagnosis often gets short shrift because of the perceived urgency of discharge. 3. The problem oriented record, in practice, has frequently led to a shift in emphasis from synthesis of findings to fragmentation of problems. 4. Presumptive diagnoses frequently metamorphose into established diagnoses in medical records, even if incorrect. 5. A number of authors have apparently disparaged the importance of diagnosis. Nonetheless, it is clear that diagnosis must continue to play a central role in clinical medicine. We propose several ways by which we can resist these forces and assure that diagnosis retains its appropriate position of primacy.

Electronic medical record-assisted design of a cluster-randomized trial to improve diabetes care and outcomes.

BACKGROUND: Electronic medical records (EMRs) have the potential to facilitate the design of large cluster-randomized trials (CRTs). OBJECTIVE: To describe the design of a CRT of clinical decision support to improve diabetes care and outcomes. METHODS: In the Diabetes Improvement Group-Intervention Trial (DIG-IT), we identified and balanced preassignment characteristics of 12,675 diabetic patients cared for by 147 physicians in 24 practices of 2 systems using the same vendor's EMR. EMR-facilitated disease management was system A's experimental intervention; system B interventions involved patient empowerment, with or without disease management. For our sample, we: (1) identified characteristics associated with response to interventions or outcomes; (2) summarized feasible partitions of 10 system A practices (2 groups) and 14 system B practices (3 groups) using intra-cluster correlation coefficients (ICCs) and standardized differences; (3) selected (blinded) partitions to effectively balance the characteristics; and (4) randomly assigned groups of practices to interventions. RESULTS: In System A, 4,306 patients, were assigned to 2 groups of practices; 8,369 patients in system B were assigned to 3 groups of practices. Nearly all baseline outcome variables and covariates were well-balanced, including several not included in the initial design. DIG-IT's balance was superior to alternative partitions based on volume, geography or demographics alone. CONCLUSIONS: EMRs facilitated rigorous CRT design by identifying large numbers of patients with diabetes and enabling fair comparisons through preassignment balancing of practice sites. Our methods can be replicated in other settings and for other conditions, enhancing the power of other translational investigations.