Assessing the accuracy of an inter-institutional automated patient-specific health problem list

<p>Abstract</p> <p>Background</p> <p>Health problem lists are a key component of electronic health records and are instrumental in the development of decision-support systems that encourage best practices and optimal patient safety. Most health problem lists require initial clinical information to be entered manually and few integrate information across care providers and institutions. This study assesses the accuracy of a novel approach to create an inter-institutional automated health problem list in a computerized medical record (MOXXI) that integrates three sources of information for an individual patient: diagnostic codes from medical services claims from all treating physicians, therapeutic indications from electronic prescriptions, and single-indication drugs.</p> <p>Methods</p> <p>Data for this study were obtained from 121 general practitioners and all medical services provided for 22,248 of their patients. At the opening of a patient's file, all health problems detected through medical service utilization or single-indication drug use were flagged to the physician in the MOXXI system. Each new arising health problem were presented as 'potential' and physicians were prompted to specify if the health problem was valid (Y) or not (N) or if they preferred to reassess its validity at a later time.</p> <p>Results</p> <p>A total of 263,527 health problems, representing 891 unique problems, were identified for the group of 22,248 patients. Medical services claims contributed to the majority of problems identified (77%), followed by therapeutic indications from electronic prescriptions (14%), and single-indication drugs (9%). Physicians actively chose to assess 41.7% (n = 106,950) of health problems. Overall, 73% of the problems assessed were considered valid; 42% originated from medical service diagnostic codes, 11% from single indication drugs, and 47% from prescription indications. Twelve percent of problems identified through other treating physicians were considered valid compared to 28% identified through study physician claims.</p> <p>Conclusion</p> <p>Automation of an inter-institutional problem list added over half of all validated problems to the health problem list of which 12% were generated by conditions treated by other physicians. Automating the integration of existing information sources provides timely access to accurate and relevant health problem information. It may also accelerate the uptake and use of electronic medical record systems.</p

Automated Detection of Systematic Off-label Drug Use in Free Text of Electronic Medical Records.

Off-label use of a drug occurs when it is used in a manner that deviates from its FDA label. Studies estimate that 21% of prescriptions are off-label, with only 27% of those uses supported by evidence of safety and efficacy. We have developed methods to detect population level off-label usage using computationally efficient annotation of free text from clinical notes to generate features encoding empirical information about drug-disease mentions. By including additional features encoding prior knowledge about drugs, diseases, and known usage, we trained a highly accurate predictive model that was used to detect novel candidate off-label usages in a very large clinical corpus. We show that the candidate uses are plausible and can be prioritized for further analysis in terms of safety and efficacy

Medication decision-making for patients with renal insufficiency in inpatient and outpatient care at a US Veterans Affairs Medical Centre: a qualitative, cognitive task analysis.

BackgroundMany studies identify factors that contribute to renal prescribing errors, but few examine how healthcare professionals (HCPs) detect and recover from an error or potential patient safety concern. Knowledge of this information could inform advanced error detection systems and decision support tools that help prevent prescribing errors.ObjectiveTo examine the cognitive strategies that HCPs used to recognise and manage medication-related problems for patients with renal insufficiency.DesignHCPs submitted documentation about medication-related incidents. We then conducted cognitive task analysis interviews. Qualitative data were analysed inductively.SettingInpatient and outpatient facilities at a major US Veterans Affairs Medical Centre.ParticipantsPhysicians, nurses and pharmacists who took action to prevent or resolve a renal-drug problem in patients with renal insufficiency.OutcomesEmergent themes from interviews, as related to recognition of renal-drug problems and decision-making processes.ResultsWe interviewed 20 HCPs. Results yielded a descriptive model of the decision-making process, comprised of three main stages: detect, gather information and act. These stages often followed a cyclical path due largely to the gradual decline of patients' renal function. Most HCPs relied on being vigilant to detect patients' renal-drug problems rather than relying on systems to detect unanticipated cues. At each stage, HCPs relied on different cognitive cues depending on medication type: for renally eliminated medications, HCPs focused on gathering renal dosing guidelines, while for nephrotoxic medications, HCPs investigated the need for particular medication therapy, and if warranted, safer alternatives.ConclusionsOur model is useful for trainees so they can gain familiarity with managing renal-drug problems. Based on findings, improvements are warranted for three aspects of healthcare systems: (1) supporting the cyclical nature of renal-drug problem management via longitudinal tracking mechanisms, (2) providing tools to alleviate HCPs' heavy reliance on vigilance and (3) supporting HCPs' different decision-making needs for renally eliminated versus nephrotoxic medications

Deep Learning for the Radiographic Detection of periodontal Bone Loss

We applied deep convolutional neural networks (CNNs) to detect periodontal bone loss (PBL) on panoramic dental radiographs. We synthesized a set of 2001 image segments from panoramic radiographs. Our reference test was the measured % of PBL. A deep feed-forward CNN was trained and validated via 10-times repeated group shuffling. Model architectures and hyperparameters were tuned using grid search. The final model was a seven-layer deep neural network, parameterized by a total number of 4,299,651 weights. For comparison, six dentists assessed the image segments for PBL. Averaged over 10 validation folds the mean (SD) classification accuracy of the CNN was 0.81 (0.02). Mean (SD) sensitivity and specificity were 0.81 (0.04), 0.81 (0.05), respectively. The mean (SD) accuracy of the dentists was 0.76 (0.06), but the CNN was not statistically significant superior compared to the examiners (p = 0.067/t-test). Mean sensitivity and specificity of the dentists was 0.92 (0.02) and 0.63 (0.14), respectively. A CNN trained on a limited amount of radiographic image segments showed at least similar discrimination ability as dentists for assessing PBL on panoramic radiographs. Dentists’ diagnostic efforts when using radiographs may be reduced by applying machine-learning based technologies

Self-management support for chronic disease in primary care: frequency of patient self-management problems and patient reported priorities, and alignment with ultimate behavior goal selection.

BackgroundTo enable delivery of high quality patient-centered care, as well as to allow primary care health systems to allocate appropriate resources that align with patients' identified self-management problems (SM-Problems) and priorities (SM-Priorities), a practical, systematic method for assessing self-management needs and priorities is needed. In the current report, we present patient reported data generated from Connection to Health (CTH), to identify the frequency of patients' reported SM-Problems and SM-Priorities; and examine the degree of alignment between patient SM-Priorities and the ultimate Patient-Healthcare team member selected Behavioral Goal.MethodsCTH, an electronic self-management support system, was embedded into the flow of existing primary care visits in 25 primary care clinics and was used to assess patient-reported SM-Problems across 12 areas, patient identified SM-Priorities, and guide the selection of a Patient-Healthcare team member selected Behavioral Goal. SM-Problems included: BMI, diet (fruits and vegetables, salt, fat, sugar sweetened beverages), physical activity, missed medications, tobacco and alcohol use, health-related distress, general life stress, and depression symptoms. Descriptive analyses documented SM-Problems and SM-Priorities, and alignment between SM-Priorities and Goal Selection, followed by mixed models adjusting for clinic.Results446 participants with ≥ one chronic diseases (mean age 55.4 ± 12.6; 58.5% female) participated. On average, participants reported experiencing challenges in 7 out of the 12 SM-Problems areas; with the most frequent problems including: BMI, aspects of diet, and physical activity. Patient SM-Priorities were variable across the self-management areas. Patient- Healthcare team member Goal selection aligned well with patient SM-Priorities when patients prioritized weight loss or physical activity, but not in other self-management areas.ConclusionParticipants reported experiencing multiple SM-Problems. While patients show great variability in their SM-Priorities, the resulting action plan goals that patients create with their healthcare team member show a lack of diversity, with a disproportionate focus on weight loss and physical activity with missed opportunities for using goal setting to create targeted patient-centered plans focused in other SM-Priority areas. Aggregated results can assist with the identification of high frequency patient SM-Problems and SM-Priority areas, and in turn inform resource allocation to meet patient needs.Trial registrationClinicalTrials.gov ID: NCT01945918

A method to quantify residents\u27 jargon use during counseling of standardized patients about cancer screening

Background Jargon is a barrier to effective patient-physician communication, especially when health literacy is low or the topic is complicated. Jargon is addressed by medical schools and residency programs, but reducing jargon usage by the many physicians already in practice may require the population-scale methods used in Quality Improvement. Objective To assess the amount of jargon used and explained during discussions about prostate or breast cancer screening. Effective communication is recommended before screening for prostate or breast cancer because of the large number of false-positive results and the possible complications from evaluation or treatment. Participants Primary care internal medicine residents. Measurements Transcripts of 86 conversations between residents and standardized patients were abstracted using an explicit-criteria data dictionary. Time lag from jargon words to explanations was measured using “statements,” each of which contains one subject and one predicate. Results Duplicate abstraction revealed reliability κ = 0.92. The average number of unique jargon words per transcript was 19.6 (SD = 6.1); the total jargon count was 53.6 (SD = 27.2). There was an average of 4.5 jargon-explanations per transcript (SD = 2.3). The ratio of explained to total jargon was 0.15. When jargon was explained, the average time lag from the first usage to the explanation was 8.4 statements (SD = 13.4). Conclusions The large number of jargon words and low number of explanations suggest that many patients may not understand counseling about cancer screening tests. Educational programs and faculty development courses should continue to discourage jargon usage. The methods presented here may be useful for feedback and quality improvement efforts