A scalable approach for critical care data extraction and analysis in an academic medical center.

BACKGROUND: Electronic health records are a valuable asset for research, but their use is challenging due to inconsistencies of records, heterogeneous formats and the distribution over multiple, non-integrated information systems. Hence, specialized health data engineering and data science expertise are required to enable research. To facilitate secondary use of clinical routine data collected in our intensive care wards, we developed a scalable approach, consisting of cohort generation, variable filtering and data extraction steps. OBJECTIVE: With this report we share our workflow of data request, cohort identification and data extraction. We present an algorithm for automatic data extraction from our critical care information system (CCIS) that can be adapted to other object-oriented data bases. METHODS: We introduced a data request process with functionalities for automated identification of patient cohorts and a specialized hierarchical data structure that supports filtering relevant variables from the CCIS and further systems for the specified cohorts. The data extraction algorithm takes patient pseudonyms and variable lists as inputs. Algorithms are implemented in Python, leveraging the PySpark framework running on our data lake infrastructure. RESULTS: Our data request process is in operational use since June 2022. Since then we have served 121 projects with 148 service requests in total. We discuss the hierarchical structure and the frequently used data items of our CCIS in detail and present an application example, including cohort selection, data extraction and data transformation into an analyses-ready format. CONCLUSIONS: Using clinical routine data for secondary research is challenging and requires an interdisciplinary team. We developed a scalable approach that automates steps for cohort identification, data extraction and common data pre-processing steps. Additionally, we facilitate data harmonization, integration and consult on typical data analysis scenarios, machine learning algorithms and visualizations in dashboards.

Assessment of expanded programme on immunization routine data quality in the upper east region of Ghana.

BACKGROUND: Data quality is a major challenge for most health institutions and organizations across the globe. The Ghana Health Service, supported by other non-governmental organizations, has instituted various strategies to address and improve data quality issues in regional and district health facilities in Ghana. This study sought to assess routine data quality of Expanded Programme on Immunization, specifically for Penta 1 and Penta 3 vaccines. METHODS: A descriptive cross-sectional study design was used for the study. A simple random sampling method was used to select thirty-four health facilities across seven sub-municipalities. Records from the Expanded Programme on Immunization (EPI) Tally Books and Monthly Vaccination Summary Report were reviewed and compared with data entered into the District Health Information Management System 2 (DHIMS2) software for the period of January to December 2020. The World Health Organization Data quality self-assessment (DQS) tool was used to compare data recorded in the EPI tally books with monthly data from summary reports and DHIMS2. Data accuracy ratio was determined by the data quality assessment tools and STATA version 14.2 was used to run additional analysis. A data discrepancy is when two corresponding data sets don't match. RESULTS: The results showed discrepancies between recounted tallies in EPI tally books and summary reports submitted as well as DHIMS2. Verification factor of 97.4% and 99.3% and a discrepancy rate of 2.6 and 0.7 for Penta 1 and Penta 3 respectively were recorded for tallied data and summary reports. A verification factor of 100.5% and 99.9% and a discrepancy of -0.5 and 0.1 respectively for the same antigens were obtained for the summary reports and DHIMS2. Data timeliness was 90.7% and completeness was 100% for both antigens. CONCLUSION: The accuracy of Penta 1 and Penta 3 data on EPI in the Upper East Region of Ghana was high. The data availability, timeliness and completeness were also high.

Evaluation of candidate data-based welfare indicators for veal calves in Switzerland.

Welfare assessment protocols have been developed for dairy cows and veal calves during the past decades. One practical use of such protocols may be conducting welfare assessments by using routinely collected digital data (i.e., data-based assessment). This approach can allow for continuous monitoring of animal welfare in a large number of farms. It recognises changes in the animal welfare status over time and enables comparison between farms. Since no comprehensive data-based assessment for veal calves is currently available, the purposes of this review are (i) to provide an overview of single existing data-based indicators for veal calves and (ii) to work out the necessary requirements for data-based indicators to be used in a comprehensive welfare assessment for veal calves in Switzerland. We used the Welfare Quality Protocol® (WQ) for veal calves and the Terrestrial Animal Health Code from the World Organisation of Animal Health for guidance throughout this process. Subsequently, routinely collected data were evaluated as data sources for welfare assessment in Swiss veal operations. The four WQ principles reflecting animal welfare, i.e., 'good feeding', 'good housing', 'good health' and 'appropriate behaviour' were scarcely reflected in routinely available data. Animal health, as one element of animal welfare, could be partially assessed using data-based indicators through evaluation of mortality, treatments, and carcass traits. No data-based indicators reflecting feeding, housing and animal behaviour were available. Thus, it is not possible to assess welfare in its multidimensionality using routinely collected digital data in Swiss veal calves to date. A major underlying difficulty is to differentiate between veal calves and other youngstock using routine data, since an identifying category for veal calves is missing in official Swiss databases. In order to infer animal welfare from routine data, adaptations of data collection strategies and animal identification are required. Data-based welfare assessment could then be used to complement on-farm assessments efficiently and, e.g., to attribute financial incentives for specifically high welfare standards accordingly.

Beating the Clock: A Prediction Model for Timely Care in Emergency Departments.

We developed and validated a statistical prediction model using 2.5 electronic health records from 24 German emergency departments (EDs) to estimate treatment timeliness at triage. The model's moderate fit and reliance on interoperable, routine data suggest its potential for implementation in ED crowding management.

Quality Assessment Framework of Clinical Routine Data for Secondary Use.

To systematically and comprehensively identify data issues in large clinical datasets, we adopted a harmonized data quality assessment framework with Python scripts before integrating the data into FHIR® for secondary use. We also added a preliminary step of categorizing data fields within the database scheme to facilitate the implementation of the data quality framework. As a result, we demonstrated the efficiency and comprehensiveness of detecting data issues using the framework. In future steps, we plan to continually utilize the framework to identify data issues and develop strategies for improving our data quality.

Does a palliative medicine service reduce hospital length of stay and costs in adults with a life-limiting illness?-a difference-in-differences evaluation of service expansion in Ireland.

BACKGROUND: People approaching end of life account disproportionately for health care costs, and the majority of these costs accrue in hospitals. The economic evidence base to improve value of care to this population is thin. Natural experiment methods may be helpful in bridging evidence gaps with credible causal estimates from routine data, but these methods have seldom been applied in this field. This study aimed to evaluate the association between timely palliative care consultation and length of stay for adults with serious illness admitted to acute hospital in Ireland. METHODS: In primary analysis we evaluated if timely palliative care receipt following emergency hospital inpatient admission impacted length of stay (LOS); in secondary analysis we verified if palliative medicine service (PMS) implementation co-occurred with any changes in in-hospital mortality, and we estimated cost differences associated with any change in LOS. This was a secondary analysis on routinely collected data for acute admissions to public hospitals in Ireland. We used difference-in-differences analysis to exploit the staggered implementation of PMS teams at acute public hospitals in Ireland between 2010 and 2015. We identified palliative care receipt following PMS implementation using ICD-10 codes, and we matched admissions involving a palliative care interaction to admissions in years prior to PMS implementation using propensity score weights. RESULTS: Our primary analytic sample included 4,314 observations, of whom 608 (14%) received timely palliative care. We estimated that the intervention reduced LOS by nearly two days, with an estimated associated saving per admission of €1,820. These analyses were robust to multiple sensitivity analyses on regression specification, weighting strategy and site selection. Proportion of admissions ending in death did not change following PMS implementation. CONCLUSIONS: Prompt interaction between suitable patients and palliative care can improve the quality and efficiency of care to this population. Many patients receive palliative care later in the hospital stay, which does not yield cost-savings. Future studies can extend and strengthen our approach with better data, as well as using different methods to understand how to trigger palliative care early in a hospital admission and realise available gains.

The electronic frailty index and outcomes in patients with myocardial infarction.

BACKGROUND: Frailty is increasingly present in patients with acute myocardial infarction. The electronic Frailty Index (eFI) is a validated method of identifying vulnerable older patients in the community from routine primary care data. Our aim was to assess the relationship between the eFI and outcomes in older patients hospitalised with acute myocardial infarction. STUDY DESIGN AND SETTING: Retrospective cohort study using the DataLoch Heart Disease Registry comprising consecutive patients aged 65 years or over hospitalised with a myocardial infarction between October 2013 and March 2021. METHODS: Patients were classified as fit, mild, moderate, or severely frail based on their eFI score. Cox-regression analysis was used to determine the association between frailty category and all-cause mortality. RESULTS: In 4670 patients (median age 77 years [71-84], 43% female), 1865 (40%) were classified as fit, with 1699 (36%), 798 (17%) and 308 (7%) classified as mild, moderate and severely frail, respectively. In total, 1142 patients died within 12 months of which 248 (13%) and 147 (48%) were classified as fit and severely frail, respectively. After adjustment, any degree of frailty was associated with an increased risk of all-cause death with the risk greatest in the severely frail (reference = fit, adjusted hazard ratio 2.87 [95% confidence intervals 2.24 to 3.66]). CONCLUSION: The eFI identified patients at high risk of death following myocardial infarction. Automatic calculation within administrative data is feasible and could provide a low-cost method of identifying vulnerable older patients on hospital presentation.

Predictor Selection for Case Complexity Based on Routine Data for Needs-Based and Competence-Based Staff Planning.

Due to nursing staff shortage and growing nursing care demand, resource allocation and optimal task distribution have become primary concerns of nursing management. Grade mix analysis based on nursing interventions and nurse qualifications from routine patient documentation can support this. Case complexity is a key linking factor of nursing interventions, workload, and grade mix. This study determined case complexity predictors based on one year of routine patient documentation (n = 3,373 cases) from a Swiss hospital and predicted the patient clinical complexity level via weighted cumulative logistic regression models. Significant predictors were sex, age, pre-admission residence, admission type, self- care index, pneumonia risk, and number of nursing interventions. The models' accuracy is limited yet appropriate for applications such as needs- and competence- based staff-planning. After calibration via in-hospital data it could support nursing management in these tasks. The next step is now to test the model in a clinical setting.

Use of proxy indicators for automated surveillance of severe acute respiratory infection, the Netherlands, 2017 to 2023: a proof-of-concept study.

BackgroundEffective pandemic preparedness requires robust severe acute respiratory infection (SARI) surveillance. However, identifying SARI patients based on symptoms is time-consuming. Using the number of reverse transcription (RT)-PCR tests or contact and droplet precaution labels as a proxy for SARI could accurately reflect the epidemiology of patients presenting with SARI.AimWe aimed to compare the number of RT-PCR tests, contact and droplet precaution labels and SARI-related International Classification of Disease (ICD)-10 codes and evaluate their use as surveillance indicators.MethodsPatients from all age groups hospitalised at Leiden University Medical Center between 1 January 2017 up to and including 30 April 2023 were eligible for inclusion. We used a clinical data collection tool to extract data from electronic medical records. For each surveillance indicator, we plotted the absolute count for each week, the incidence proportion per week and the correlation between the three surveillance indicators.ResultsWe included 117,404 hospital admissions. The three surveillance indicators generally followed a similar pattern before and during the COVID-19 pandemic. The correlation was highest between contact and droplet precaution labels and ICD-10 diagnostic codes (Pearson correlation coefficient: 0.84). There was a strong increase in the number of RT-PCR tests after the start of the COVID-19 pandemic.DiscussionAll three surveillance indicators have advantages and disadvantages. ICD-10 diagnostic codes are suitable but are subject to reporting delays. Contact and droplet precaution labels are a feasible option for automated SARI surveillance, since these reflect trends in SARI incidence and may be available real-time.

Corrigendum: Estimating the prevalence of alcohol-related disorders and treatment utilization in Bremen 2016/2017 through routine data linkage.

[This corrects the article DOI: 10.3389/fpsyt.2023.1002526.].

Implementing a Biomedical Data Warehouse From Blueprint to Bedside in a Regional French University Hospital Setting: Unveiling Processes, Overcoming Challenges, and Extracting Clinical Insight.

Background: Biomedical data warehouses (BDWs) have become an essential tool to facilitate the reuse of health data for both research and decisional applications. Beyond technical issues, the implementation of BDWs requires strong institutional data governance and operational knowledge of the European and national legal framework for the management of research data access and use. Objective: In this paper, we describe the compound process of implementation and the contents of a regional university hospital BDW. Methods: We present the actions and challenges regarding organizational changes, technical architecture, and shared governance that took place to develop the Nantes BDW. We describe the process to access clinical contents, give details about patient data protection, and use examples to illustrate merging clinical insights. Unlabelled: More than 68 million textual documents and 543 million pieces of coded information concerning approximately 1.5 million patients admitted to CHUN between 2002 and 2022 can be queried and transformed to be made available to investigators. Since its creation in 2018, 269 projects have benefited from the Nantes BDW. Access to data is organized according to data use and regulatory requirements. Conclusions: Data use is entirely determined by the scientific question posed. It is the vector of legitimacy of data access for secondary use. Enabling access to a BDW is a game changer for research and all operational situations in need of data. Finally, data governance must prevail over technical issues in institution data strategy vis-à-vis care professionals and patients alike.

Clustering care pathways of people with alcohol dependence using a data linkage of routine data in Bremen, Germany.

BACKGROUND: Although many individuals with alcohol dependence (AD) are recognized in the German healthcare system, only a few utilize addiction-specific treatment services. Those who enter treatment are not well characterized regarding their prospective pathways through the highly fragmented German healthcare system. This paper aims to (1) identify typical care pathways of patients with AD and their adherence to treatment guidelines and (2) explore the characteristics of these patients using routine data from different healthcare sectors. METHODS: We linked routinely collected register data of individuals with a documented alcohol-related diagnosis in the federal state of Bremen, Germany, in 2016/2017 and their addiction-specific health care: two statutory health insurance funds (outpatient pharmacotherapy for relapse prevention and inpatient episodes due to AD with and without qualified withdrawal treatment (QWT)), the German Pension Insurance (rehabilitation treatment) and a group of communal hospitals (outpatient addiction care). Individual care pathways of five different daily states of utilized addiction-specific treatment following an index inpatient admission due to AD were analyzed using state sequence analysis and cluster analysis. The follow-up time was 307 days (10 months). Individuals of the clustered pathways were compared concerning current treatment recommendations (1: QWT followed by postacute treatment; 2: time between QWT and rehabilitation). Patients' characteristics not considered during the cluster analysis (sex, age, nationality, comorbidity, and outpatient addiction care) were then compared using a multinomial logistic regression. RESULTS: The analysis of 518 individual sequences resulted in the identification of four pathway clusters differing in their utilization of acute and postacute treatment. Most did not utilize subsequent addiction-specific treatment after their index inpatient episode (n = 276) or had several inpatient episodes or QWT without postacute treatment (n = 205). Two small clusters contained pathways either starting rehabilitation (n = 26) or pharmacotherapy after the index episode (n = 11). Overall, only 9.3% utilized postacute treatment as recommended. CONCLUSIONS: A concern besides the generally low utilization of addiction-specific treatment is the implementation of postacute treatments for individuals after QWT.

Using Routine Data to Improve Lesbian, Gay, Bisexual, and Transgender Health.

The collection of sexual orientation in routine data, generated either from contacts with health services or in infrastructure data resources designed and collected for policy and research, has improved substantially in the United Kingdom in the last decade. Inclusive measures of gender and transgender status are now also beginning to be collected. This viewpoint considers current data collections, and their strengths and limitations, including accessing data, sample size, measures of sexual orientation and gender, measures of health outcomes, and longitudinal follow-up. The available data are considered within both sociopolitical and biomedical models of health for individuals who are lesbian, gay, bisexual, transgender, queer, or of other identities including nonbinary (LGBTQ+). Although most individual data sets have some methodological limitations, when put together, there is now a real depth of routine data for LGBTQ+ health research. This paper aims to provide a framework for how these data can be used to improve health and health care outcomes. Four practical analysis approaches are introduced-descriptive epidemiology, risk prediction, intervention development, and impact evaluation-and are discussed as frameworks for translating data into research with the potential to improve health.

Exploring the validity of routine individuated service data for antenatal HIV surveillance in the Western Cape.

Background: In the Western Cape, South Africa, public-sector individual-level routine data are consolidated from multiple sources through the Provincial Health Data Centre (PHDC). This enables the description of temporal changes in population-wide antenatal HIV seroprevalence. We evaluated the validity of these data compared to aggregated program data and population-wide sentinel antenatal HIV seroprevalence surveys for the Western Cape province. Methods: We conducted a retrospective cohort analysis of all pregnancies identified in the PHDC from January 2011 to December 2020. Evidence of antenatal and HIV care from electronic platforms were linked using a unique patient identifier. HIV prevalence estimates were triangulated and compared with available survey estimates and aggregated programmatic data from registers as recorded in the District Health Information System. Provincial, district-level and age-group HIV prevalence estimates were compared between data systems using correlation coefficients, absolute differences and trend analysis. Results: Of the 977800 pregnancies ascertained, PHDC HIV prevalence estimates from 2011-2013 were widely disparate from aggregate and survey data (due to incomplete electronic data), whereas from 2014 onwards, estimates were within the 95% confidence interval of survey estimates, and closely correlated to aggregate data estimates (r = 0.8; p = 0.01), with an average prevalence difference of 0.4%. PHDC data show a slow but steady increase in provincial HIV prevalence from 16.7% in 2015 to 18.6% in 2020. The highest HIV prevalence was in the Cape Metro district (20.3%) Prevalence estimates by age group were comparable between sentinel surveys and PHDC from 2015 onwards, with prevalence estimates stable over time among younger age-groups (15-24 years) but increased among older age-groups (> 34 years). Conclusions: This study compares sentinel seroprevalence surveys with both register-based aggregate data and consolidated individuated administrative data. We show that in this setting linked individuated data may be reliably used for HIV surveillance and provide more granular estimates with greater efficiency than seroprevalence surveys and register-based aggregate data.

Travelling numbers and broken loops: A qualitative systematic review on collecting and reporting maternal and neonatal health data in low-and lower-middle income countries.

Data and indicator estimates are considered vital to document persisting challenges in maternal and newborn health and track progress towards global goals. However, prioritization of standardised, comparable quantitative data can preclude the collection of locally relevant information and pose overwhelming burdens in low-resource settings, with negative effects on the provision of quality of care. A growing body of qualitative studies aims to provide a place-based understanding of the complex processes and human experiences behind the generation and use of maternal and neonatal health data. We conducted a qualitative systematic review exploring how national or international requirements to collect and report data on maternal and neonatal health indicators are perceived and experienced at the sub-national and country level in low-income and lower-middle income countries. We systematically searched six electronic databases for qualitative and mixed-methods studies published between January 2000 and March 2023. Following screening of 4084 records by four reviewers, 47 publications were included in the review. Data were analysed thematically and synthesised from a Complex Adaptive Systems (CAS) theoretical perspective. Our findings show maternal and neonatal health data and indicators are not fixed, neutral entities, but rather outcomes of complex processes. Their collection and uptake is influenced by a multitude of system hardware elements (human resources, relevancy and adequacy of tools, infrastructure, and interoperability) and software elements (incentive systems, supervision and feedback, power and social relations, and accountability). When these components are aligned and sufficiently supportive, data and indicators can be used for positive system adaptivity through performance evaluation, prioritization, learning, and advocacy. Yet shortcomings and broken loops between system components can lead to unforeseen emergent behaviors such as blame, fear, and data manipulation. This review highlights the importance of measurement approaches that prioritize local relevance and feasibility, necessitating participatory approaches to define context-specific measurement objectives and strategies.

Understanding Pathways into Care-homes using Data (UnPiCD study): a two-part model to estimate inpatient and care-home costs using national linked health and social care data.

BACKGROUND: Pathways into care-homes have been under-researched. Individuals who move-in to a care-home from hospital are clinically distinct from those moving-in from the community. However, it remains unclear whether the source of care-home admission has any implications in term of costs. Our aim was to quantify hospital and care-home costs for individuals newly moving-in to care homes to compare those moving-in from hospital to those moving-in from the community. METHODS: Using routinely-collected national social care and health data we constructed a cohort including people moving into care-homes from hospital and community settings between 01/04/2013-31/03/2015 based on records from the Scottish Care-Home Census (SCHC). Individual-level data were obtained from Scottish Morbidity Records (SMR01/04/50) and death records from National Records of Scotland (NRS). Unit costs were identified from NHS Scotland costs data and care-home costs from the SCHC. We used a two-part model to estimate costs conditional on having incurred positive costs. Additional analyses estimated differences in costs for the one-year period preceding and following care-home admission. RESULTS: We included 14,877 individuals moving-in to a care-home, 8,472 (57%) from hospital, and 6,405 (43%) from the community. Individuals moving-in to care-homes from the community incurred higher costs at £27,117 (95% CI £ 26,641 to £ 27,594) than those moving-in from hospital with £24,426 (95% CI £ 24,037 to £ 24,814). Hospital costs incurred during the year preceding care-home admission were substantially higher (£8,323 (95% CI£8,168 to £8,477) compared to those incurred after moving-in to care-home (£1,670 (95% CI£1,591 to £1,750). CONCLUSION: Individuals moving-in from hospital and community have different needs, and this is reflected in the difference in costs incurred. The reduction in hospital costs in the year after moving-in to a care-home indicates the positive contribution of care-home residency in supporting those with complex needs. These data provide an important contribution to inform capacity planning on care provision for adults with complex needs and the costs of care provision.

Prospective study validating a multidimensional treatment decision score predicting the 24-month outcome in untreated patients with clinically isolated syndrome and early relapsing-remitting multiple sclerosis, the ProVal-MS study.

INTRODUCTION: In Multiple Sclerosis (MS), patients´ characteristics and (bio)markers that reliably predict the individual disease prognosis at disease onset are lacking. Cohort studies allow a close follow-up of MS histories and a thorough phenotyping of patients. Therefore, a multicenter cohort study was initiated to implement a wide spectrum of data and (bio)markers in newly diagnosed patients. METHODS: ProVal-MS (Prospective study to validate a multidimensional decision score that predicts treatment outcome at 24 months in untreated patients with clinically isolated syndrome or early Relapsing-Remitting-MS) is a prospective cohort study in patients with clinically isolated syndrome (CIS) or Relapsing-Remitting (RR)-MS (McDonald 2017 criteria), diagnosed within the last two years, conducted at five academic centers in Southern Germany. The collection of clinical, laboratory, imaging, and paraclinical data as well as biosamples is harmonized across centers. The primary goal is to validate (discrimination and calibration) the previously published DIFUTURE MS-Treatment Decision score (MS-TDS). The score supports clinical decision-making regarding the options of early (within 6 months after study baseline) platform medication (Interferon beta, glatiramer acetate, dimethyl/diroximel fumarate, teriflunomide), or no immediate treatment (> 6 months after baseline) of patients with early RR-MS and CIS by predicting the probability of new or enlarging lesions in cerebral magnetic resonance images (MRIs) between 6 and 24 months. Further objectives are refining the MS-TDS score and providing data to identify new markers reflecting disease course and severity. The project also provides a technical evaluation of the ProVal-MS cohort within the IT-infrastructure of the DIFUTURE consortium (Data Integration for Future Medicine) and assesses the efficacy of the data sharing techniques developed. PERSPECTIVE: Clinical cohorts provide the infrastructure to discover and to validate relevant disease-specific findings. A successful validation of the MS-TDS will add a new clinical decision tool to the armamentarium of practicing MS neurologists from which newly diagnosed MS patients may take advantage. Trial registration ProVal-MS has been registered in the German Clinical Trials Register, `Deutsches Register Klinischer Studien` (DRKS)-ID: DRKS00014034, date of registration: 21 December 2018; https://drks.de/search/en/trial/DRKS00014034.

Characterising trends in the initiation, timing, and completion of recommended summary plan for emergency care and treatment (ReSPECT) plans: Retrospective analysis of routine data from a large UK hospital trust.

AIM: To assess patient socio-demographic and disease characteristics associated with the initiation, timing, and completion of emergency care and treatment planning in a large UK-based hospital trust. METHODS: Secondary retrospective analysis of data across 32 months extracted from digitally stored Recommended Summary Plan for Emergency Care and Treatment (ReSPECT) plans within the electronic health record system of an acute hospital trust in England, UK. RESULTS: Data analysed from ReSPECT plans (n = 23,729), indicate an increase in the proportion of admissions having a plan created from 4.2% in January 2019 to 6.9% in August 2021 (mean = 8.1%). Forms were completed a median of 41 days before death (a median of 58 days for patients with capacity, and 21 days for patients without capacity). Do not attempt cardiopulmonary resuscitation was more likely to be recorded for patients lacking capacity, with increasing age (notably for patients aged over 74 years), being female and the presence of multiple disease groups. 'Do not attempt cardiopulmonary resuscitation' was less likely to be recorded for patients having ethnicity recorded as Asian or Asian British and Black or Black British compared to White. Having a preferred place of death recorded as 'hospital' led to a five-fold increase in the likelihood of dying in hospital. CONCLUSION: Variation in the initiation, timing, and completion of ReSPECT plans was identified by applying an evaluation framework. Digital storage of ReSPECT plan data presents opportunities for assessing trends and completion of the ReSPECT planning process and benchmarking across sites. Further research is required to monitor and understand any inequity in the implementation of the ReSPECT process in routine care.

Quality of care in patients with hypertension: a retrospective cohort study of primary care routine data in Germany.

BACKGROUND: Hypertension is a leading cause of morbidity and mortality if not properly managed. Primary care has a major impact on these outcomes if its strengths, such as continuity of care, are deployed wisely. The analysis aimed to evaluate the quality of care for newly diagnosed hypertension in routine primary care data. METHODS: In the retrospective cohort study, routine data (from 2016 to 2022) from eight primary care practices in Germany were exported in anonymized form directly from the electronic health record (EHR) systems and processed for this analysis. The analysis focused on five established quality indicators for the care of patients who have been recently diagnosed with hypertension. RESULTS: A total of 30,691 patients were treated in the participating practices, 2,507 of whom have recently been diagnosed with hypertension. Prior to the pandemic outbreak, 19% of hypertensive patients had blood pressure above 140/90 mmHg and 68% received drug therapy (n = 1,372). After the pandemic outbreak, the proportion of patients with measured blood pressure increased from 63 to 87%, while the other four indicators remained relatively stable. Up to 80% of the total variation of the quality indicators could be explained by individual practices. CONCLUSION: For the majority of patients, diagnostic procedures are not used to the extent recommended by guidelines. The analysis showed that quality indicators for outpatient care could be mapped onto the basis of routine data. The results could easily be reported to the practices in order to optimize the quality of care.

[Evaluation of electronic medical records from general practices using the example of the diagnosis of community-acquired pneumonia: A qualitative feasibility study]. / Nutzung von Daten aus dem Praxisverwaltungssystem eines großen hausärztlichen Verbundes medizinischer Versorgungszentren am Beispiel der Diagnosestellung einer ambulant erworbenen Pneumonie ­ eine qualitative Machbarkeitsstudie.

BACKGROUND: Data collected by general practitioners (GPs) may provide potential for health services research. In this study, we investigated if clinical questions can be answered with GPs' electronic medical records (EMRs) by means of diagnosing community-acquired pneumonia (CAP). METHOD: Patients diagnosed with CAP, defined as ICD code J18.9, were identified in the fourth quarter of 2021. The data were derived from the EMR system (Medical Office®) of a central German association of 30 general practices, using three different approaches: 1. The integrated statistic tool was used to record whether patients were referred for radiological diagnostic confirmation. 2. Retrospectively, EMRs were evaluated manually by a doctor familiar with the EMR. 3. The raw data of the EMR system were extracted by automated export. The information obtained through the three types of access was compared. For each patient case, detailed comments on problems and specifics were documented and evaluated by qualitative content analysis (QCA) according to Mayring. RESULTS: In total, 164 patients diagnosed with CAP were identified. The numbers of documented radiological diagnostic confirmations varied between data approaches: While the manual evaluation of the EMRs revealed 60 referred patients, the statistics tool identified 38 of these cases. The export of the raw data identified 58 referrals to radiography after adjustment. According to QCA, there was a high variation in applied diagnostics and time of diagnosis. Referrals for radiography were made both before and after coding of the diagnosis. In case of hospitalization, X-rays were usually performed during the inpatient stay. Laboratory tests were performed as an alternative to radiography. There was also a high variation in the documentation of risk factors and diagnostic certainty. DISCUSSION: The statistics tool integrated into the EMR system is a quick way to perform simple queries but proved to be impracticable for complex questions. The EMRs provide detailed information but need to be evaluated manually. An automated data export from the raw data offers both detailed information and access to large volumes of data but requires complex preparation and appropriate IT expertise. CONCLUSION: Based on the example of diagnosed CAP in a GP setting, the use of data extracted from an EMR system seems to be feasible to answer simple clinical questions. However, it is necessary to adapt the data export, and a comparison with a small number of manually evaluated cases is useful to achieve valid results.