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Diagnosis of venous thromboembolism (VTE) is often delayed, and facilitating earlier diagnosis may improve associated morbidity and mortality. Clinical notes contain information not found elsewhere in the medical record that could facilitate timely VTE diagnosis and accurate quality measurement. However, extracting relevant information from unstructured clinical notes is complex. Today, there are relatively few electronic clinical quality measures (eCQMs) in our national payment program and none that use natural language processing (NLP) techniques for data extraction. NLP holds great promise for making quality measurement more accurate and more efficient. Given the potential of NLP-based applications to facilitate more accurate VTE detection, primary care is one clinical setting in urgent need of this type of tool. This study aimed to develop a tool that extracts VTE symptoms from clinical notes for use within an eCQM to quantify the rate of delayed diagnosis of VTE in primary care settings. We iteratively developed an NLP-based data extraction tool, venous thromboembolism symptom extractor (VTExt), on an internal dataset using a rule-based approach to extract VTE symptoms from primary care clinical note text. The VTE symptoms lexicon was derived and optimized with physician guidance and externally validated using datasets from 2 independent health care organizations. We performed 26 rounds of performance evaluation of notes sampled from the case cohort (17,585 patient progress note sentences from 279 patient notes), and 5 rounds of evaluation of the control cohort (2838 patient progress note sentences from 50 patient notes). VTExt's performance was evaluated using evaluation metrics, including area under the curve, positive predictive value, negative predictive value, sensitivity, and specificity. VTExt achieved near-perfect performance in extracting VTE symptoms from primary care notes sampled from records of patients diagnosed with or without VTE. In external validation, VTExt achieved promising performance in 2 additional geographically distant organizations using different electronic health record systems. When compared against a deep learning model and 4 machine learning models, VTExt exhibited similar or even improved performance across all metrics. This study demonstrates a data-driven NLP-based approach to clinical note information extraction that can be generalized to different electronic health record systems across different institutions. Due to the robust performance of this tool, VTExt is the first NLP application to be used in a nationally endorsed eCQM.

Background One of the primary obstacles to measure clinical quality is the lack of configurable solutions to make computers understand and compute clinical quality indicators. The paper presents a solution that can help clinical staff develop clinical quality measurement more easily and generate the corresponding data reports and visualization by a configurable method based on openEHR and Clinical Quality Language (CQL). Methods First, expression logic adopted from CQL was combined with openEHR to express clinical quality indicators. Archetype binding provides the clinical information models used in expression logic, terminology binding makes the medical concepts consistent used in clinical quality artifacts and metadata is regarded as the essential component for sharing and management. Then, a systematic approach was put forward to facilitate the development of clinical quality indicators and the generation of corresponding data reports and visualization. Finally, clinical physicians were invited to test our system and give their opinions. Results With the combination of openEHR and CQL, 64 indicators from Centers for Medicare & Medicaid Services (CMS) were expressed for verification and a complicated indicator was shown as an example. 68 indicators from 17 different scenes in the local environment were also expressed and computed in our system. A platform was built to support the development of indicators in a unified way. Also, an execution engine can parse and compute these indicators. Based on a clinical data repository (CDR), indicators were used to generate data reports and visualization and shown in a dashboard. Conclusion Our method is capable of expressing clinical quality indicators formally. With the computer-interpretable indicators, a systematic approach can make it more easily to define clinical indicators and generate medical data reports and visualization, and facilitate the adoption of clinical quality measurements.

Henrik Toft Sørensen,1 Lars Pedersen,1 Jørgen Jørgensen,2 Vera Ehrenstein1 1Department of Clinical Epidemiology, Aarhus University Hospital, Aarhus, 2Danish Regions, Copenhagen, Denmark The health care systems in many countries are facing several challenges: the aging population,the need for personalized medicine, evolving treatment modalities, quality andsafety imperatives, and unsustainable costs, to mention only a few. Population aging in the face of significant pressure to contain costs is perhaps the most immediate challenge. The proportion of people aged 65 years or older in Western Europe and North America is expected to increase to 26% in 2025. Furthermore, clinical medicine in the Western world is confronting an evolving set of diseases, as smoking becomes less prevalent and obesity more common. Diagnostics and treatment of chronic disease have improved, while the threshold for initiating preventive treatment of asymptomatic conditions has been lowered. Consequently, the number of patients with multimorbidity, that is, the coexistence of several chronic diseases, will increase dramatically.1 To treat a disease or prevent its progression, patients with several chronic diseases often take multiple drugs, each with potentially severe side effects. In patients with multiple morbidities, “polypharmacy” is a challenging clinical issue, often associated with iatrogenic harm.2 A call for innovative approaches to polypharmacy has been the focus of recent editorials in high-impact medical journals.3–5 Randomized trials rarely address multimorbidity, adherence to treatments, co-intervention (polypharmacy), or their long-term risks.6 These challenges underscore the need for population-based long-term longitudinal clinical data available for clinical care and research.

The Danish Cerebral Palsy Follow-up Program is a combined follow-up program and national clinical quality database that aims to monitor and improve the quality of health care for children with cerebral palsy (CP). The database includes children with CP aged 0-15 years and children with symptoms of CP aged 0-5 years. In the follow-up program, the children are offered examinations throughout their childhood by orthopedic surgeons, physiotherapists, occupational therapists, and pediatricians. Examinations of gross and fine motor function, manual ability, muscle tone, passive range of motion, use of orthotics, and assistive devices are performed once a year; radiographic examination of the hips is planned based on the child's age and gross motor function; and the diagnosis is performed once before the age of 5 years. Six indicators were developed based on scientific literature and consensus in the steering committee, and their calculation is based on the following four main variables: radiographic examination of the hip, gross motor function, manual ability, and diagnosis. The 2014 annual report includes results of the quality indicators in three of five regions in Denmark comprising 432 children with CP, corresponding to a coverage of 82% of the expected population. The Danish Cerebral Palsy Follow-up Program is currently under development as a national clinical quality database in Denmark. The database holds potential for research in prevalence, clinical characteristics of the population, and the effects of prevention and treatment.

Purpose The purpose of this paper is to assess the relationship between patient satisfaction and a variety of clinical quality measures in an ambulatory setting to determine if there is significant overlap between patient satisfaction and clinical quality or if they are separate domains of overall physician quality. Assessing this relationship will help to determine whether there is congruence between different types of clinical quality performance and patient satisfaction and therefore provide insight to appropriate financial structures for physicians. Design/methodology/approach Ordered probit regression analysis is conducted with overall rating of physician from patient satisfaction responses to the Clinician and Groups Consumer Assessment of Healthcare Providers and Systems survey as the dependent variable. Physician clinical quality is measured across five composite groups based on 26 Healthcare Effectiveness Data and Information Set (HEDIS) measures aggregated from patient electronic health records. Physician and patient demographic variables are also included in the model. Findings Better physician performance on HEDIS measures are correlated with increases in patient satisfaction for three composite measures: antibiotics, generics, and vaccination; it has no relationship for chronic conditions and is correlated with decrease in patient satisfaction for preventative measures, although the negative relationship for preventative measures is not robust in sensitivity analysis. In addition, younger physicians and male physicians have higher satisfaction scores even with the HEDIS quality measures in the regression. Research limitations/implications There are four primary limitations to this study. First, the data for the study come from a single hospital provider organization. Second, the survey response rate for the satisfaction measure is low. Third, the physician clinical quality measure is the percent of the physician’s relevant patient population that met the HEDIS measure rather than if the measure was met for the individual patient. Finally, it is not possible to distinguish if the significant coefficient estimates on the physician age and gender variables are capturing systematic differences in physician behavior or capturing patient bias. Practical implications The results suggest patient satisfaction and physician clinical quality may be complementary, capturing similar aspects of overall physician quality, across some clinical quality measures but for other measures satisfaction and clinical quality are unrelated or negatively related. Therefore, for some clinical quality metrics, it will be important to separately compensate clinical quality and satisfaction and understand the relationship between metrics. Finally, the strong relationship between the level of patient satisfaction and physician age, physician gender, and patient age are important to consider when designing a physician compensation package based on patient satisfaction; if these differences reflect patient bias they could increase inequality among medical staff if compensation is based on patient satisfaction. Originality/value This study is the first to use physician organization data to examine patient satisfaction and physician performance on a variety of HEDIS quality metrics.

Background and purpose The Registry of Stroke Care Quality (RES‐Q) is a worldwide quality improvement data platform that captures performance and quality measures, enabling standardized comparisons of hospital care. The aim of this study was to determine if, and how, RES‐Q data are used to influence stroke quality improvement and identify the support and educational needs of clinicians using RES‐Q data to improve stroke care. Methods A cross‐sectional self‐administered online survey was administered (October 2021–February 2022). Participants were RES‐Q hospital local coordinators responsible for stroke data collection. Descriptive statistics are presented. Results Surveys were sent to 1463 hospitals in 74 countries; responses were received from 358 hospitals in 55 countries (response rate 25%). RES‐Q data were used “always” or “often” to: develop quality improvement initiatives (n = 213, 60%); track stroke care quality over time (n = 207, 58%); improve local practice (n = 191, 53%); and benchmark against evidence‐based policies, procedures and/or guidelines to identify practice gaps (n = 179, 50%). Formal training in the use of RES‐Q tools and data were the most frequent support needs identified by respondents (n = 165, 46%). Over half “strongly agreed” or “agreed” that to support clinical practice change, education is needed on: (i) using data to identify evidence–practice gaps (n = 259, 72%) and change clinical practice (n = 263, 74%), and (ii) quality improvement science and methods (n = 255, 71%). Conclusion RES‐Q data are used for monitoring stroke care performance. However, to facilitate their optimal use, effective quality improvement methods are needed. Educating staff in quality improvement science may develop competency and improve use of data in practice.

The aim of the database, which has existed for registration of all patients with colorectal cancer in Denmark since 2001, is to improve the prognosis for this patient group. All Danish patients with newly diagnosed colorectal cancer who are either diagnosed or treated in a surgical department of a public Danish hospital. The database comprises an array of surgical, radiological, oncological, and pathological variables. The surgeons record data such as diagnostics performed, including type and results of radiological examinations, lifestyle factors, comorbidity and performance, treatment including the surgical procedure, urgency of surgery, and intra- and postoperative complications within 30 days after surgery. The pathologists record data such as tumor type, number of lymph nodes and metastatic lymph nodes, surgical margin status, and other pathological risk factors. The database has had >95% completeness in including patients with colorectal adenocarcinoma with >54,000 patients registered so far with approximately one-third rectal cancers and two-third colon cancers and an overrepresentation of men among rectal cancer patients. The stage distribution has been more or less constant until 2014 with a tendency toward a lower rate of stage IV and higher rate of stage I after introduction of the national screening program in 2014. The 30-day mortality rate after elective surgery has been reduced from >7% in 2001-2003 to <2% since 2013. The database is a national population-based clinical database with high patient and data completeness for the perioperative period. The resolution of data is high for description of the patient at the time of diagnosis, including comorbidities, and for characterizing diagnosis, surgical interventions, and short-term outcomes. The database does not have high-resolution oncological data and does not register recurrences after primary surgery. The Danish Colorectal Cancer Group provides high-quality data and has been documenting an increase in short- and long-term survivals since it started in 2001 for both patients with colon and rectal cancers.