Electronic Health Record Problem List Research Articles

Evaluation of SNOMED CT Grouper Accuracy and Coverage in Organizing the Electronic Health Record Problem List by Clinical System: Observational Study.

The problem list (PL) is a repository of diagnoses for patients' medical conditions and health-related issues. Unfortunately, over time, our PLs have become overloaded with duplications, conflicting entries, and no-longer-valid diagnoses. The lack of a standardized structure for review adds to the challenges of clinical use. Previously, our default electronic health record (EHR) organized the PL primarily via alphabetization, with other options available, for example, organization by clinical systems or priority settings. The system's PL was built with limited groupers, resulting in many diagnoses that were inconsistent with the expected clinical systems or not associated with any clinical systems at all. As a consequence of these limited EHR configuration options, our PL organization has poorly supported clinical use over time, particularly as the number of diagnoses on the PL has increased. We aimed to measure the accuracy of sorting PL diagnoses into PL system groupers based on Systematized Nomenclature of Medicine Clinical Terms (SNOMED CT) concept groupers implemented in our EHR. We transformed and developed 21 system- or condition-based groupers, using 1211 SNOMED CT hierarchal concepts refined with Boolean logic, to reorganize the PL in our EHR. To evaluate the clinical utility of our new groupers, we extracted all diagnoses on the PLs from a convenience sample of 50 patients with 3 or more encounters in the previous year. To provide a spectrum of clinical diagnoses, we included patients from all ages and divided them by sex in a deidentified format. Two physicians independently determined whether each diagnosis was correctly attributed to the expected clinical system grouper. Discrepancies were discussed, and if no consensus was reached, they were adjudicated by a third physician. Descriptive statistics and Cohen κ statistics for interrater reliability were calculated. Our 50-patient sample had a total of 869 diagnoses (range 4-59; median 12, IQR 9-24). The reviewers initially agreed on 821 system attributions. Of the remaining 48 items, 16 required adjudication with the tie-breaking third physician. The calculated κ statistic was 0.7. The PL groupers appropriately associated diagnoses to the expected clinical system with a sensitivity of 97.6%, a specificity of 58.7%, a positive predictive value of 96.8%, and an F1-score of 0.972. We found that PL organization by clinical specialty or condition using SNOMED CT concept groupers accurately reflects clinical systems. Our system groupers were subsequently adopted by our vendor EHR in their foundation system for PL organization.

Identifying Children in Foster Care and Improving Foster Care Documentation in Primary Care.

Children and youth in foster care (CYFC) are a population with special healthcare needs, and the American Academy of Pediatrics has healthcare standards to care for this population, but implementation challenges include identifying clinic patients in foster care (FC). Documentation of FC status in the Electronic Health Record (EHR) can support the identification of CYFC to tailor care delivery. Therefore, we aimed to improve the percentage of CYFC with problem list (PL) documentation of FC status from 20% to 60% within 12 months. This study used a five-cycle plan-do-study-act quality improvement model in two co-located primary care teaching clinics. The primary outcome was the weekly percentage of patients with FC status on EHR PL. Ishikawa cause and effect analysis and resident survey identified barriers and informed interventions: education, patient list distribution, documentation training, email reminders, and clinic champion. We constructed statistical process control charts of the primary outcome to assess for improvement. Mean weekly percentage of patients with FC status on PL improved from 19.8% to 60.2%. The most extensive improvements occurred after designating a clinic champion and providing email reminders with enhanced patient lists. The sustainability of PL documentation (mean = 71.7%) was demonstrated 3-4 years after the completion of plan-do-study-act cycle interventions. Educating providers, collaborating with child welfare to provide patient lists to providers, standardizing documentation, and designating clinic champions are promising methods of improving EHR documentation of FC status. Identifying and documenting FC status are important initial steps to optimizing care for this vulnerable population in primary care.

Improvement in Palivizumab Administration Prior to Discharge for Hospitalized Infants with Hemodynamically Significant Congenital Heart Disease: A Quality Improvement Initiative.

In this quality improvement initiative, we aimed to increase provider adherence with palivizumab administration guidelines for hospitalized infants with hemodynamically significant congenital heart disease. We included 470 infants over four respiratory syncytial virus (RSV) seasons from 11/2017 to 03/2021 (baseline season: 11/2017-03/2018). Interventions included the following: education, including palivizumab in the sign-out template, identifying a pharmacy expert, and a text alert (seasons 1 and 2: 11/2018-03/2020) that was replaced by an electronic health record (EHR) best practice alert (BPA) in season 3 (11/2020-03/2021). The text alert and BPA prompted providers to add "Need for RSV immunoprophylaxis" to the EHR problem list. The outcome metric was the percentage of eligible patients administered palivizumab prior to discharge. The process metric was the percentage of eligible patients with "Need for RSV immunoprophylaxis" on the EHR problem list. The balancing metric was the percentage of palivizumab doses administered to ineligible patients. A statistical process control P-chart was used to analyze the outcome metric. The mean percentage of eligible patients who received palivizumab prior to hospital discharge increased significantly from 70.1% (82/117) to 90.0% (86/96) in season 1 and to 97.9% (140/143) in season 3. Palivizumab guideline adherence was as high or higher for those with "Need for RSV immunoprophylaxis" on the problem list than for those without it in most time periods. The percentage of inappropriate palivizumab doses decreased from 5.7% (n = 5) at baseline to 4.4% (n = 4) in season 1 and 0.0% (n = 0) in season 3. Through this initiative, we improved adherence with palivizumab administration guidelines for eligible infants prior to hospital discharge.

Detection of Cognitive Impairment after Critical Illness with the Medicare Annual Wellness Visit: A Cohort Study.

Rationale: Cognitive impairment after critical illness is common in observational studies of older intensive care unit (ICU) survivors. The rate of screening for and diagnosis of cognitive impairment in ICU survivors in nonresearch settings is unknown. Objectives: To determine how often cognitive impairment was detected in older adults in the year after critical illness at an academic medical center as part of 1) the Medicare Annual Wellness Visit (AWV) and 2) routine clinical care. Methods: This study was a retrospective cohort study conducted at an urban academic medical center. The study included 696 patients aged 65 years and older admitted to the medical ICU between October 1, 2016, and October 1, 2018, and discharged alive. Patients were also required to have a health system-affiliated primary care provider. Patients were followed for 1 year. We defined cognitive impairment detected in the AWV as either an indicated diagnosis of cognitive impairment or dementia or patient, family, or provider indication of memory concerns during the AWV. We modeled the incidence of AWV completion and the detection of cognitive impairment using semiparametric additive models accounting for the competing risk of death. Results: Over 1 year of follow-up, the cumulative incidence of mortality was 23.0% (95% confidence interval [CI], 19.9-26.1%), with 24.7% (95% CI, 21.5-27.9%) completing the AWV. The cumulative incidence of cognitive impairment first detected through the AWV was 3.4% (95% CI, 1.8-5.0%) at 1 year, with a higher cumulative incidence for diagnoses of cognitive impairment or dementia first indicated via encounter diagnosis codes or the electronic health record problem list (5.9%; 95% CI, 3.9-7.9%). Conclusions: The results of our study suggest that the currently implemented AWV is unlikely to be an adequate mechanism for detecting cognitive impairment in a high-risk population such as those recovering from critical illness.

Diagnosing Obesity as a First Step to Weight Loss: An Observational Study.

ObjectiveThis study aimed to explore the relationship between an obesity diagnosis and weight loss as a percentage of total body weight loss over 9 to 15 months, using electronic health record data.MethodsAn observational study of 688,878 adult patients at 15 health systems with BMI ≥ 30 kg/m2 examined the relationship between weight loss and documentation of obesity diagnosis. Multivariable logistic regression models were created using a stepwise backwards elimination procedure to identify potential predictors of weight loss.ResultsOf patients with BMI ≥ 30, 44.9% had an obesity diagnosis on a claim or electronic health record problem list; 16.9% and 5.9% lost ≥ 5% and ≥ 10% of their body weight, respectively. Multivariable logistic regression models revealed a diagnosis of obesity on the same day as the initial weight (odds ratio [OR] = 1.3; CI: 1.2‐1.3; P < 0.001) as a predictor of ≥ 5% total body weight loss in 9 to 15 months. Other significant predictors included an antiobesity medication prescription, female sex, diagnosis of type 2 diabetes, Medicare/Medicaid insurance, and number of ambulatory visits.ConclusionsWhile controlling for potentially confounding factors, documentation of an obesity diagnosis remained independently predictive of at least 5% weight loss. This suggests that documenting a diagnosis of obesity may be an important step toward engaging patients to lose weight.

276 Low Rates of Colonoscopic Surveillance Among Patients With High Risk Adenomas

INTRODUCTION: There have been a myriad of efforts to address suboptimal colorectal cancer (CRC) screening rates in the United States (US). Individuals with high-risk colon polyps detected during screening colonoscopy have 2- to 5-fold increased risk for the future development of CRC; however, there are few efforts directed towards optimizing risk reduction in this group. We examined surveillance uptake for patients with high-risk polyps in a large, academic healthcare system. METHODS: \We identified a cohort of patients: 1) age 50–75; 2) with a colonoscopic diagnosis of high-risk adenoma (HRA; 1 adenoma &gt;1 cm, adenoma with villous histology, adenoma with high-grade dysplasia, or 3 or more adenomas) between January 2013 and January 2016; and 3) regular primary care (PC) at UCLA Health. We collected information from the electronic health record (EHR) on demographics, polyp histology, colonoscopy completion, and office visits. We determined the proportion of patients that completed surveillance colonoscopy each year and used Student's t-tests and chi-square tests to evaluate univariate associations between patient characteristics and colonoscopy completion. RESULTS: Our cohort included 203 individuals with HRA. Mean age was 62.5 (SD = 7.6), 62.6% were male, and 75.3% were White (Table 1). There were 153 (75.4%) patients who did not complete surveillance within 3 years. Surveillance completion increased from 24.6% to 40.4% by 3.5 years. 110 (54.2%) completed surveillance by the end of 5 years (Table 2). In all, 202 (99.5%) patients had documentation of a PC visit and 165 (81.3%) had documentation of a GI visit before surveillance was due. There were 121 (59.6%) patients with a documented history of adenoma in the EHR problem list. Patients with ≥1 GI visit after HRA diagnosis (165, 81.3%) were more likely to complete surveillance than patients without a GI visit after HRA diagnosis (38, 18.7%) (P &lt; 0.001). CONCLUSION: Overall uptake of surveillance colonoscopy was low at 3 years for patients with HRA. There was notable improvement in follow-up by 3.5 years; however, adherence remained suboptimal at 40%. Clinical implications of this delay are unknown. Documentation of adenoma history was low. Individuals with HRA represent a group that has been largely neglected in CRC prevention/control research and that warrants evidence-based strategies to ensure appropriate follow-up to reduce CRC burden.

Link between prescriptions and the electronic health record.

The extent to which medication prescriptions had a reason for the medication use documented in form of a potential indication within the electronic health record (EHR) problem lists using a MEDication Indication (MEDI) resource was evaluated. Prescriptions from January 1 to June 30, 2015, comparing them to patients' problem lists using MEDI and the MEDI High Precision Subset (MEDI-HPS) were analyzed. RxNorm generic ingredient name codes in MEDI were used to map prescriptions to problems using codes from the International Classification of Diseases, 9th edition. A reference standard was established to evaluate the MEDI precision and recall by having 2 pharmacists independently manually review prescriptions and problem lists from 30 randomly selected patients. For 62,191 patients, 61% of prescriptions matched a potential indication on the patient's problem list using MEDI, whereas only 38% had a match using MEDI-HPS. The precision of MEDI compared to the reference standard was 47% with a recall of 57%, whereas MEDI-HPS had a precision of 79% and recall of 96%. Secondary analysis excluding medication prescribed with a supply of ≤14 days gave slightly better, yet not significant, results. Analysis of patient records found most patients did not have an indication listed in the EHR problem list that would match a specific medication, suggesting that the problem lists may be incomplete. When using MEDI, 61% of prescriptions matched to the problem list, compared with only 38% using MEDI-HPS. Likewise, 37% of problems matched to prescriptions when using MEDI, compared with only 21% using MEDI-HPS.

Provider Perspectives on Advance Care Planning Documentation in the Electronic Health Record: The Experience of Primary Care Providers and Specialists Using Advance Health-Care Directives and Physician Orders for Life-Sustaining Treatment.

Advance care planning (ACP) is valued by patients and clinicians, yet documenting ACP in an accessible manner is problematic. In order to understand how providers incorporate electronic health record (EHR) ACP documentation into clinical practice, we interviewed providers in primary care and specialty departments about ACP practices (n = 13) and analyzed EHR data on 358 primary care providers (PCPs) and 79 specialists at a large multispecialty group practice. Structured interviews were conducted with 13 providers with high and low rates of ACP documentation in primary care, oncology, pulmonology, and cardiology departments. The EHR problem list data on Advance Health Care Directives (AHCDs) and Physician Orders for Life-Sustaining Treatment (POLST) were used to calculate ACP documentation rates. Examining seriously ill patients ≥65 years with no preexisting ACP documentation seen by providers during 2013 to 2014, 88.6% (AHCD) and 91.1% (POLST) of 79 specialists had zero ACP documentations. Of 358 PCPs, 29.1% (AHCD) and 62.3% (POLST) had zero ACP documentations. Interviewed PCPs often believed ACP documentation was beneficial and accessible, while specialists more often did not. Specialists expressed more confusion about documenting ACP, whereas PCPs reported standard clinic workflows. Problems with interoperability between outpatient and inpatient EHR systems and lack of consensus about who should document ACP were sources of variations in practices. Results suggest that providers desire standardized workflows for ACP discussion and documentation. New Medicare reimbursement for ACP and an increasing number of quality metrics for ACP are incentives for health-care systems to address barriers to ACP documentation.

A rules based algorithm to generate problem lists using emergency department medication reconciliation

ObjectivesTo evaluate the sensitivity and specificity of a problem list automatically generated from the emergency department (ED) medication reconciliation. MethodsWe performed a retrospective cohort study of patients admitted via the ED who also had a prior inpatient admission within the past year of an academic tertiary hospital. Our algorithm used the First Databank ontology to group medications into therapeutic classes, and applied a set of clinically derived rules to them to predict obstructive lung disease, hypertension, diabetes, congestive heart failure (CHF), and thromboembolism (TE) risk. This prediction was compared to problem lists in the last discharge summary in the electronic health record (EHR) as well as the emergency attending note. ResultsA total of 603 patients were enrolled from 03/29/2013–04/30/2013. The algorithm had superior sensitivity for all five conditions versus the attending problem list at the 99% confidence level (Obstructive Lung Disease 0.93 vs 0.47, Hypertension 0.93 vs 0.56, Diabetes 0.97 vs 0.73, TE Risk 0.82 vs 0.36, CHF 0.85 vs 0.38), while the attending problem list had superior specificity for both hypertension (0.76 vs 0.94) and CHF (0.87 vs 0.98). The algorithm had superior sensitivity for all conditions versus the EHR problem list (Obstructive Lung Disease 0.93 vs 0.34, Hypertension 0.93 vs 0.30, Diabetes 0.97 vs 0.67, TE Risk 0.82 vs 0.23, CHF 0.85 vs 0.32), while the EHR problem list also had superior specificity for detecting hypertension (0.76 vs 0.95) and CHF (0.87 vs 0.99). ConclusionThe algorithm was more sensitive than clinicians for all conditions, but less specific for conditions that are not treated with a specific class of medications. This suggests similar algorithms may help identify critical conditions, and facilitate thorough documentation, but further investigation, potentially adding alternate sources of information, may be needed to reliably detect more complex conditions.

Comparing Patient-Reported Medical Problems with the Electronic Health Record Problem List

Electronic health record (EHR) problem lists are extremely important, and yet they are often incomplete and out of date. We compared the EHR problem list to a self-reported problem list obtained via a tablet-administered questionnaire to identify potential synergy. We conducted a retrospective review comparing the EHR problem list to the patients’ self-reported problem list during the year of 2011. To confirm the accuracy of patient self-reports, we also analyzed medication lists, and laboratory results for two selected conditions, hypercholesterolemia and diabetes mellitus. Overall, 1472 patients at the Massachusetts General Hospital (MGH) used the tablet questionnaire. Of these, 843 (57.27%) had no problem reported in the EHR and some problem reported on the tablet (“Tablet Only”); 42 patients (2.85%) had no problem reported on the tablet or in the EHR (“None”); 17 patients (1.15%) had some problem reported in the EHR and no problem reported on the tablet (“LMR Only”); and 570 patients (38.72%) had some problem reported both on the tablet and in the LMR (“Both”). Overall, we studied 59 conditions, of which twelve had enough patients to run Chi Square analysis. Of the 12 conditions analyzed, 10 were significant, and 9 out of 10 conditions favored the tablet-administered questionnaire. Medication lists and laboratory results were reviewed to confirm the presence of the selected conditions. We reviewed the EHR problem list one year after the study was initiated to update the conditions under study. The additional data corroborated 107 additional self-reported conditions in 97 patients. In summary, a self-administered tablet questionnaire is an acceptable method for collecting the medical history. When combined with the EHR problem list, self-reported medical history is optimal for obtaining the most accurate problem list possible.

Design of a cluster-randomized trial of electronic health record-based tools to address overweight and obesity in primary care.

Primary care providers often fail to identify patients who are overweight or obese or discuss weight management with them. Electronic health record-based tools may help providers with the assessment and management of overweight and obesity. We describe the design of a trial to examine the effectiveness of electronic health record-based tools for the assessment and management of overweight and obesity among adult primary care patients, as well as the challenges we encountered. We developed several new features within the electronic health record used by primary care practices affiliated with Brigham and Women's Hospital in Boston, MA. These features included (1) reminders to measure height and weight, (2) an alert asking providers to add overweight or obesity to the problem list, (3) reminders with tailored management recommendations, and (4) a Weight Management screen. We then conducted a pragmatic, cluster-randomized controlled trial in 12 primary care practices. We randomized 23 clinical teams ("clinics") within the practices to the intervention group (n = 11) or the control group (n = 12). The new features were activated only for clinics in the intervention group. The intervention was implemented in two phases: the height and weight reminders went live on 15 December 2011 (Phase 1), and all of the other features went live on 11 June 2012 (Phase 2). Study enrollment went from December 2011 through December 2012, and follow-up ended in December 2013. The primary outcomes were 6-month and 12-month weight change among adult patients with body mass index ≥25 who had a visit at one of the primary care clinics during Phase 2. Secondary outcome measures included the proportion of patients with a recorded body mass index in the electronic health record, the proportion of patients with body mass index ≥25 who had a diagnosis of overweight or obesity on the electronic health record problem list, and the proportion of patients with body mass index ≥25 who had a follow-up appointment about their weight or were prescribed weight loss medication. We encountered challenges in our development of an intervention within the existing structure of an electronic health record. For example, although we decided to randomize clinics within primary care practices, this decision may have introduced contamination and led to some imbalance of patient characteristics between the intervention and control practices. Using the electronic health record as the primary data source reduced the cost of the study, but not all desired data were recorded for every participant. Despite the challenges, this study should provide valuable information about the effectiveness of electronic health record-based tools for addressing overweight and obesity in primary care.

Problem list documentation and surveillance mammography: Can meaningful use be useful?

e17696 Background: The electronic health record problem list (ePL) plays important roles in clinical decision support and communication with consequent improved care delivery. Despite established n...

Chronic Kidney Disease in an Electronic Health Record Problem List: Quality of Care, ESRD, and Mortality

Background: Whether chronic kidney disease (CKD) recognition in an electronic health record (EHR) problem list improves processes of care or clinical outcomes of end-stage renal disease (ESRD) and death is unclear. Methods: We identified patients who had at least 1 year of follow-up (2005-2009) in our EHR-based CKD registry (n = 25,742). CKD recognition was defined by having ICD-9 codes for CKD, diabetic kidney disease, or hypertensive kidney disease in the problem list. We calculated proportions of patients with and without CKD recognition and examined differences by demographics, clinical factors, and development of ESRD or mortality. We evaluated differences in the proportion of patients with CKD-specific laboratory results checked before and after recognition among cases and propensity-matched controls. Results: Only 11% (n = 2,735) had CKD recognition in the problem list and they were younger (68 vs. 71 years), a higher proportion were male (61 vs. 37%) and African-American (21 vs. 10%) compared to those unrecognized. CKD-specific laboratory results for patients with estimated glomerular filtration rate (eGFR) 30-59 including intact parathyroid hormone (23 vs. 6%), vitamin D (22 vs. 18%), phosphorus (29 vs. 7%), and a urine check for proteinuria (55 vs. 36%) were significantly more likely to be done among those with CKD recognition (all p < 0.05). Similar results were found for eGFR <30 except for proteinuria and in our propensity score-matched control analysis. There was no independent association of CKD recognition with ESRD or mortality. Conclusions: CKD recognition in the EHR problem list was low, but translated into more CKD-specific processes of care; however ESRD or mortality were not affected.

Electronic Health Record Use to Classify Patients with Newly Diagnosed versus Preexisting Type 2 Diabetes: Infrastructure for Comparative Effectiveness Research and Population Health Management

Use of electronic health record (EHR) content for comparative effectiveness research (CER) and population health management requires significant data configuration. A retrospective cohort study was conducted using patients with diabetes followed longitudinally (N=36,353) in the EHR deployed at outpatient practice networks of 2 health care systems. A data extraction and classification algorithm targeting identification of patients with a new diagnosis of type 2 diabetes mellitus (T2DM) was applied, with the main criterion being a minimum 30-day window between the first visit documented in the EHR and the entry of T2DM on the EHR problem list. Chart reviews (N=144) validated the performance of refining this EHR classification algorithm with external administrative data. Extraction using EHR data alone designated 3205 patients as newly diagnosed with T2DM with classification accuracy of 70.1%. Use of external administrative data on that preselected population improved classification accuracy of cases identified as new T2DM diagnosis (positive predictive value was 91.9% with that step). Laboratory and medication data did not help case classification. The final cohort using this 2-stage classification process comprised 1972 patients with a new diagnosis of T2DM. Data use from current EHR systems for CER and disease management mandates substantial tailoring. Quality between EHR clinical data generated in daily care and that required for population health research varies. As evidenced by this process for classification of newly diagnosed T2DM cases, validation of EHR data with external sources can be a valuable step.

A first step towards translating evidence into practice: heart failure in a community practice-based research network

To determine the validity of an electronic health record (EHR) in the identification of patients with left ventricular dysfunction in a primary care setting. A cross-sectional study. Nine clinics participating from the Providence Research Network (PRN) comprising 75 physicians serving approximately 200,000 patients. All clinics utilise the Logician EHR for all patient care activities. The study included all PRN patients with an active chart. All patients with a heart failure diagnosis in the problem list were identified by database query. Left ventricular ejection fraction (LVEF) data were identified through query of local cardiology and hospital echocardiography databases. Additional LVEF data were sought in a manual search of paper charts. To determine the problem list coding accuracy for a heart failure (HF) diagnosis we evaluated sensitivity, positive predictive value and related derived statistical measures using documented LVEF as the 'gold standard'. Of 205 755 active PRN patients, 1731 were identified with a problem list entry of HF. Based on comparison with documented LVEF, the sensitivity for problem list entry was 43.9% and 54.4% when HF was defined as an LVEF < or = 55% and < or = 40%, respectively. The validity of an EHR problem list entry of HF was poor. The problem list validity could be enhanced through reconciliation with other data sources. Inaccurate EHR problem lists may have clinical consequences, including underprescribing of beneficial therapies.