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Clinical coding is critical for hospital reimbursement, quality assessment, and health care planning. In Scandinavia, however, coding is often done by junior doctors or medical secretaries, leading to high rates of coding errors. Artificial intelligence (AI) tools, particularly semiautomatic computer-assisted coding tools, have the potential to reduce the excessive burden of administrative and clinical documentation. To date, much of what we know regarding these tools comes from lab-based evaluations, which often fail to account for real-world complexity and variability in clinical text. This study aims to investigate whether an AI tool developed by by Norwegian Centre for E-health Research at the University Hospital of North Norway, Easy-ICD (International Classification of Diseases), can enhance clinical coding practices by reducing coding time and improving data quality in a realistic setting. We specifically examined whether improvements differ between long and short clinical notes, defined by word count. An AI tool, Easy-ICD, was developed to assist clinical coders and was tested for improving both accuracy and time in a 1:1 crossover randomized controlled trial conducted in Sweden and Norway. Participants were randomly assigned to 2 groups (Sequence AB or BA), and crossed over between coding longer texts (Period 1; mean 307, SD 90; words) versus shorter texts (Period 2; mean 166, SD 55; words), while using our tool versus not using our tool. This was a purely web-based trial, where participants were recruited through email. Coding time and accuracy were logged and analyzed using Mann-Whitney U tests for each of the 2 periods independently, due to differing text lengths in each period. The trial had 17 participants enrolled, but only data from 15 participants (300 coded notes) were analyzed, excluding 2 incomplete records. Based on the Mann-Whitney U test, the median coding time difference for longer clinical text sequences was 123 seconds (P<.001, 95% CI 81-164), representing a 46% reduction in median coding time when our tool was used. For shorter clinical notes, the median time difference of 11 seconds was not significant (P=.25, 95% CI -34 to 8). Coding accuracy improved with Easy-ICD for both longer (62% vs 67%) and shorter clinical notes (60% vs 70%), but these differences were not statistically significant (P=.50and P=.17, respectively). User satisfaction ratings (submitted for 37% of cases) showed slightly higher approval for the tool's suggestions on longer clinical notes. This study demonstrates the potential of AI to transform common tasks in clinical workflows, with ostensible positive impacts on work efficiencies for clinical coding tasks with more demanding longer text sequences. Further studies within hospital workflows are required before these presumed impacts can be more clearly understood.

In China, sample-based mortality surveillance systems, such as the Chinese Center for Disease Control and Prevention's disease surveillance points system and the Ministry of Health's vital registration system, have been used for decades to provide nationally representative data on health status for health-care decision-making and performance evaluation. However, neither system provided representative mortality and cause-of-death data at the provincial level to inform regional health service needs and policy priorities. Moreover, the systems overlapped to a considerable extent, thereby entailing a duplication of effort. In 2013, the Chinese Government combined these two systems into an integrated national mortality surveillance system to provide a provincially representative picture of total and cause-specific mortality and to accelerate the development of a comprehensive vital registration and mortality surveillance system for the whole country. This new system increased the surveillance population from 6 to 24% of the Chinese population. The number of surveillance points, each of which covered a district or county, increased from 161 to 605. To ensure representativeness at the provincial level, the 605 surveillance points were selected to cover China's 31 provinces using an iterative method involving multistage stratification that took into account the sociodemographic characteristics of the population. This paper describes the development and operation of the new national mortality surveillance system, which is expected to yield representative provincial estimates of mortality in China for the first time.

The North Central London (NCL) Cancer Alliance carried out a quality improvement (QI) project to fill a distinct knowledge gap regarding the quality of clinical coded data in a primary care electronic health care record system across the whole cancer pathway. This study aims to establish the quality of cancer-related clinical coding in NCL primary care, encompassing both quantitative measures (eg, coding completeness and diversity) and qualitative dimensions such as clinical relevance and workflow alignment. This was a mixed methods QI project in which we combined an observational dataset review and qualitative data from stakeholder interviews, workshops, and discussions. In the dataset review, we evaluated completeness, diversity, validation, and granularity in cancer clinical coding along the patient cancer pathway, which was split into three domains: (1) patient characteristics and risk factors, (2) cancer screening attendance, and (3) living with cancer. It was conducted in NCL primary care electronic health record systems, covering a population of over 1.4 million adults across 5 boroughs. Cancer-related clinical coding in NCL primary care revealed significant gaps despite high completeness for ethnicity (912,679/1,055,083, 86.5%) and language (898,023/1,307,601, 68.7%). Employment status (29,848/1,229,644, 2.4%) and family history of cancer (183,424/1,236,580, 14.8%) were underrecorded, with wide variation in coding practices. Screening data showed good alignment with national datasets for cervical and bowel screening but fragmented and inconsistent breast screening data due to a lack of standardized codes. Cancer diagnosis coding was incomplete (4604/5260, 87.5% recorded), and treatment and staging data were almost entirely absent, limiting proactive management of long-term consequences. Stakeholder input highlighted inconsistent template use, limited data updates, and insufficient incentives as key barriers to better coding. The QI project has provided a detailed insight into the many dimensions of cancer coding and sheds light on many factors that underpin variation and coding preference. We offer a number of recommendations. The prioritized ones include the need for a cancer clinical coding data framework for primary care supported by appropriate funding and incentivization; improvements in the breast screening pathway and its interface with primary care; improvements in the quality of secondary care information that is sent to primary care; and dissemination of the importance of coding of cancer activity in primary care.

Background While clinical coding is intended to be an objective and standardized practice, it is important to recognize that it is not entirely the case. The clinical and bureaucratic practices from event of death to a case being entered into a research dataset are important context for analysing and interpreting this data. Variation in practices can influence the accuracy of the final coded record in two different stages: the reporting of the death certificate, and the International Classification of Diseases (Version 10; ICD-10) coding of that certificate. Methods This study investigated 91,022 deaths recorded in the Scottish Asthma Learning Healthcare System dataset between 2000 and 2017. Asthma-related deaths were identified by the presence of any of ICD-10 codes J45 or J46, in any position. These codes were categorized either as relating to asthma attacks specifically (status asthmatic; J46) or generally to asthma diagnosis (J45). Results We found that one in every 200 deaths in this were coded as being asthma related. Less than 1% of asthma-related mortality records used both J45 and J46 ICD-10 codes as causes. Infection (predominantly pneumonia) was more commonly reported as a contributing cause of death when J45 was the primary coded cause, compared to J46, which specifically denotes asthma attacks. Conclusion Further inspection of patient history can be essential to validate deaths recorded as caused by asthma, and to identify potentially mis-recorded non-asthma deaths, particularly in those with complex comorbidities.

Introduction Routinely collected data sets are increasingly used for research, financial reimbursement and health service planning. High quality data are necessary for reliable analysis. This study aims to assess the published accuracy of routinely collected data sets in Great Britain. Methods Systematic searches of the EMBASE, PUBMED, OVID and Cochrane databases were performed from 1989 to present using defined search terms. Included studies were those that compared routinely collected data sets with case or operative note review and those that compared routinely collected data with clinical registries. Results Thirty-two studies were included. Twenty-five studies compared routinely collected data with case or operation notes. Seven studies compared routinely collected data with clinical registries. The overall median accuracy (routinely collected data sets versus case notes) was 83.2% (IQR: 67.3-92.1%). The median diagnostic accuracy was 80.3% (IQR: 63.3-94.1%) with a median procedure accuracy of 84.2% (IQR: 68.7-88.7%). There was considerable variation in accuracy rates between studies (50.5-97.8%). Since the 2002 introduction of Payment by Results, accuracy has improved in some respects, for example primary diagnoses accuracy has improved from 73.8% (IQR: 59.3-92.1 %) to 96.0% (IQR: 89.3-96.3), P=0.020. Conclusion Accuracy rates are improving. Current levels of reported accuracy suggest that routinely collected data are sufficiently robust to support their use for research and managerial decision-making.

ObjectiveAnalysis of routinely collected electronic health record (EHR) data from primary care is reliant on the creation of codelists to define clinical features of interest. To improve scientific rigour, transparency and replicability, we describe and demonstrate a standardised reproducible methodology for clinical codelist development.DesignWe describe a three-stage process for developing clinical codelists. First, the clear definition a priori of the clinical feature of interest using reliable clinical resources. Second, development of a list of potential codes using statistical software to comprehensively search all available codes. Third, a modified Delphi process to reach consensus between primary care practitioners on the most relevant codes, including the generation of an ‘uncertainty’ variable to allow sensitivity analysis.SettingThese methods are illustrated by developing a codelist for shortness of breath in a primary care EHR sample, including modifiable syntax for commonly used statistical software.ParticipantsThe codelist was used to estimate the frequency of shortness of breath in a cohort of 28 216 patients aged over 18 years who received an incident diagnosis of lung cancer between 1 January 2000 and 30 November 2016 in the Clinical Practice Research Datalink (CPRD).ResultsOf 78 candidate codes, 29 were excluded as inappropriate. Complete agreement was reached for 44 (90%) of the remaining codes, with partial disagreement over 5 (10%). 13 091 episodes of shortness of breath were identified in the cohort of 28 216 patients. Sensitivity analysis demonstrates that codes with the greatest uncertainty tend to be rarely used in clinical practice.ConclusionsAlthough initially time consuming, using a rigorous and reproducible method for codelist generation ‘future-proofs’ findings and an auditable, modifiable syntax for codelist generation enables sharing and replication of EHR studies. Published codelists should be badged by quality and report the methods of codelist generation including: definitions and justifications associated with each codelist; the syntax or search method; the number of candidate codes identified; and the categorisation of codes after Delphi review.

Objective To determine the completeness and diagnostic validity of myocardial infarction recording across four national health record sources in primary care, hospital care, a disease registry, and mortality register. Design Cohort study. Participants 21 482 patients with acute myocardial infarction in England between January 2003 and March 2009, identified in four prospectively collected, linked electronic health record sources: Clinical Practice Research Datalink (primary care data), Hospital Episode Statistics (hospital admissions), the disease registry MINAP (Myocardial Ischaemia National Audit Project), and the Office for National Statistics mortality register (cause specific mortality data). Setting One country (England) with one health system (the National Health Service). Main outcome measures Recording of acute myocardial infarction, incidence, all cause mortality within one year of acute myocardial infarction, and diagnostic validity of acute myocardial infarction compared with electrocardiographic and troponin findings in the disease registry (gold standard). Results Risk factors and non-cardiovascular coexisting conditions were similar across patients identified in primary care, hospital admission, and registry sources. Immediate all cause mortality was highest among patients with acute myocardial infarction recorded in primary care, which (unlike hospital admission and disease registry sources) included patients who did not reach hospital, but at one year mortality rates in cohorts from each source were similar. 5561 (31.0%) patients with non-fatal acute myocardial infarction were recorded in all three sources and 11 482 (63.9%) in at least two sources. The crude incidence of acute myocardial infarction was underestimated by 25-50% using one source compared with using all three sources. Compared with acute myocardial infarction defined in the disease registry, the positive predictive value of acute myocardial infarction recorded in primary care was 92.2% (95% confidence interval 91.6% to 92.8%) and in hospital admissions was 91.5% (90.8% to 92.1%). Conclusion Each data source missed a substantial proportion (25-50%) of myocardial infarction events. Failure to use linked electronic health records from primary care, hospital care, disease registry, and death certificates may lead to biased estimates of the incidence and outcome of myocardial infarction. Trial registration NCT01569139 clinicaltrials.gov.

Conducting longitudinal health research about people experiencing homelessness poses unique challenges. Identification through administrative data permits large, cost-effective studies; however, case validity in Ontario is unknown after a 2018 Canada-wide policy change mandating homelessness coding in hospital databases. We validated case definitions for identifying homelessness using Ontario health administrative databases after introduction of this coding mandate. We assessed 42 case definitions in a representative sample of people experiencing homelessness in Toronto (n = 640) from whom longitudinal housing history (ranging from 2018 to 2022) was obtained, and a randomly selected sample of presumably housed people (n = 128,000) in Toronto. We evaluated sensitivity, specificity, positive and negative predictive values, and positive likelihood ratios to select an optimal definition, and compared the resulting true positives against false positives and false negatives to identify potential causes of misclassification. The optimal case definition included any homelessness indicator during a hospital-based encounter within 180 days of a period of homelessness (sensitivity = 52.9%; specificity = 99.5%). For periods of homelessness with ≥1 hospital-based healthcare encounter, the optimal case definition had greatly improved sensitivity (75.1%) while retaining excellent specificity (98.5%). Review of false positives suggested that homeless status is sometimes erroneously carried forward in healthcare databases after an individual transitioned out of homelessness. Case definitions to identify homelessness using Ontario health administrative data exhibit moderate to good sensitivity and excellent specificity. Sensitivity has more than doubled since the implementation of a national coding mandate. Mandatory collection and reporting of homelessness information within administrative data present invaluable opportunities for advancing research on the health and healthcare needs of people experiencing homelessness. •Homelessness status in Canadian healthcare data historically had low sensitivity.•Beginning April 2018, Canadian hospitals must code homelessness where on the chart.•Case definitions now have moderate to good sensitivity, doubling from before 2018.•Future potential improvements include updating status after homelessness ends.•Canadian healthcare data are an important resource for studies about this population.