Usage of allergy codes in primary care electronic health records: a national evaluation in Scotland

Deriving a Standardised Recommended Respiratory Disease Codelist Repository for Future Research

Background

Electronic health record (EHR) databases provide rich, longitudinal data on interactions with healthcare providers and can be used to advance research into respiratory conditions. However, since these data are primarily collected to support health care delivery, clinical coding can be inconsistent, resulting in inherent challenges in using these data for research purposes.

Methods

We systematically searched existing international literature and UK code repositories to find respiratory disease codelists for asthma from January 2018, and chronic obstructive pulmonary disease and respiratory tract infections from January 2020, based on prior searches. Medline searches using key terms provided in article lists. Full-text articles, supplementary files, and reference lists were examined for codelists, and codelists repositories were searched. A reproducible methodology for codelists creation was developed with recommended lists for each disease created based on multidisciplinary expert opinion and previously published literature.

Results

Medline searches returned 1126 asthma articles, 70 COPD articles, and 90 respiratory infection articles, with 3%, 22% and 5% including codelists, respectively. Repository searching returned 12 asthma, 23 COPD, and 64 respiratory infection codelists. We have systematically compiled respiratory disease codelists and from these derived recommended lists for use by researchers to find the most up-to-date and relevant respiratory disease codelists that can be tailored to individual research questions.

Conclusion

Few published papers include codelists, and where published diverse codelists were used, even when answering similar research questions. Whilst some advances have been made, greater consistency and transparency across studies using routine data to study respiratory diseases are needed.

Diagnostic codes for low back pain, nomenclature or noise? A descriptive study of disease classification system coding of low back pain

AIM

To compare and contrast the diagnostic codes for spinal causes of low back pain (LBP) in 3 disease classification systems (International Classification of Diseases [ICD]-10, International Classification of Primary Care [ICPC]-2 PLUS and Systematized Nomenclature of Medicine Clinical Terms - Australia [SNOMED CT-AU]) and consider how well they are aligned with the diagnostic approach recommended in contemporary clinical practice guidelines for LBP.

METHOD

This was a descriptive study which included 3 disease classification systems: ICD-10, ICPC-2 PLUS and SNOMED CT-AU. Two independent authors extracted relevant LBP codes from each system and mapped the codes to 3 guideline-endorsed categories of spine-related diagnoses for LBP (specific spinal pathology, radicular syndromes, and non-specific LBP) and the various clinical conditions (sub-categories) within each of the 3 categories.

RESULTS

ICD-10, ICPC-2 PLUS, and SNOMED CT-AU had 126, 118 and 100 codes for LBP, respectively. All systems provided codes that would cover the 3 guideline-endorsed categories of spine-related diagnoses for LBP. On the basis of contemporary guidelines, the authors developed lists of discrete sub-categories of specific spinal pathology (56 sub-categories), radicular syndromes (7 sub-categories), and non-specific LBP (10 sub-categories). Each of the classification systems was then mapped against these sub-categories to tally redundancy and determine exhaustiveness. However, no system covered all 73 sub-categories of LBP, and within each system, there was substantial redundancy with up to 22 codes for the same clinical condition.

CONCLUSION

LBP diagnostic codes used in popular disease classification systems are out of touch with current approaches to diagnosis, as reflected in contemporary LBP guidelines. Our findings suggest these disease classification systems need revision, but precisely how they should be revised is unclear.

Data quality in primary care, Scotland

BACKGROUND AND AIMS

This project explores primary care data quality (DQ) across Scotland.

METHODS AND RESULTS

A survey was sent to primary care staff in winter 2019. National data regarding Quality and Outcomes Framework (QOF) performance indicators and the GP software system used was obtained, analysed with T-tests and Chi-squared tests. Overall QOF performance with non-financial incentives from 918 practices was 77%. There was no significant difference with overall QOF performance against GP system (p = 0.46) or if the practice had a coder (p = 0.06). From the survey, search systems that make it hard to search for particular codes was the most important barrier to DQ; 61% of respondents (n = 491) felt there was particular information GPs should code, 16% of respondents stated that hospital discharge letters generally include corresponding codes and 9% for outpatient correspondence; 43% stated their practice had undertaken steps to improve DQ, training was the most common initiative, followed by workflow optimisation, dedicated coder(s), audit, guidelines and using code lists; 80% (n = 475) of respondents had received training in using their GP system, an average of eight years ago.

CONCLUSION

Obtaining improved GP systems, training, agreeing what GPs should code and improving transfer of data should be explored.

Coding and classifying GP data: the POLAR project

BACKGROUND

Data, particularly 'big' data are increasingly being used for research in health. Using data from electronic medical records optimally requires coded data, but not all systems produce coded data.

OBJECTIVE

To design a suitable, accurate method for converting large volumes of narrative diagnoses from Australian general practice records to codify them into SNOMED-CT-AU. Such codification will make them clinically useful for aggregation for population health and research purposes.

METHOD

The developed method consisted of using natural language processing to automatically code the texts, followed by a manual process to correct codes and subsequent natural language processing re-computation. These steps were repeated for four iterations until 95% of the records were coded. The coded data were then aggregated into classes considered to be useful for population health analytics.

RESULTS

Coding the data effectively covered 95% of the corpus. Problems with the use of SNOMED CT-AU were identified and protocols for creating consistent coding were created. These protocols can be used to guide further development of SNOMED CT-AU (SCT). The coded values will be immensely useful for the development of population health analytics for Australia, and the lessons learnt applicable elsewhere.

Global Trends in Anaphylaxis Epidemiology and Clinical Implications

The true global scale of anaphylaxis remains elusive, because many episodes occur in the community without presentation to health care facilities, and most regions have not yet developed reliable systems with which to monitor severe allergic events. The most robust data sets currently available are based largely on hospital admissions, which are limited by inherent issues of misdiagnosis, misclassification, and generalizability. Despite this, there is convincing evidence of a global increase in rates of all-cause anaphylaxis, driven largely by medication- and food-related anaphylaxis. There is no evidence of parallel increases in global all-cause anaphylaxis mortality, with surprisingly similar estimates for case-fatality rates at approximately 0.5% to 1% of fatal outcomes for hospitalizations due to anaphylaxis across several regions. Studying regional patterns of anaphylaxis to certain triggers have provided valuable insights into susceptibility and sensitizing events: for example, the link between the mAb cetuximab and allergy to mammalian meat. Likewise, data from published fatality registers can identify potentially modifiable risk factors that can be used to inform clinical practice, such as prevention of delayed epinephrine administration, correct posturing during anaphylaxis, special attention to populations at risk (such as the elderly on multiple medications), and use of venom immunotherapy in individuals at risk of insect-related anaphylaxis.

Anaphylaxis - Lessons learnt when East meets West

The rapidly increasing prevalence of allergic disorders over the past 2 decades highlights the need to understand the epidemiology of anaphylaxis. In Europe, the United States, and Australia, the incidence of anaphylaxis is estimated to be between 60 and 950 cases per 100 000 population, with a lifetime prevalence of anaphylaxis of 0.05%-2%. The incidence appears to be increasing over time. Although the existing Asian literature is heterogeneous and limited by under-reporting, it also suggests a similar increasing trend in anaphylaxis incidence in Asia. Anaphylaxis triggers in Asia, such as the predominance of shellfish and wheat in older children and adolescents, differ from those seen in Western populations. Triggers unique to Asia such as traditional Chinese medications, galacto-oligosaccharides, and food delicacies have also been reported. Low usage of adrenaline as first-line treatment of anaphylaxis is evident across all countries and is particularly concerning. There is a need to establish prospective, standardized protocols for anaphylaxis data collection and reporting, to enhance the collective understanding of anaphylaxis and its burden, gaps in management and to identify areas for future research and intervention in each region. Understanding of the underlying reasons explaining the difference between East and West will facilitate future primary preventive strategies.

Possible Sources of Bias in Primary Care Electronic Health Record Data Use and Reuse

BACKGROUND

Enormous amounts of data are recorded routinely in health care as part of the care process, primarily for managing individual patient care. There are significant opportunities to use these data for other purposes, many of which would contribute to establishing a learning health system. This is particularly true for data recorded in primary care settings, as in many countries, these are the first place patients turn to for most health problems.

OBJECTIVE

In this paper, we discuss whether data that are recorded routinely as part of the health care process in primary care are actually fit to use for other purposes such as research and quality of health care indicators, how the original purpose may affect the extent to which the data are fit for another purpose, and the mechanisms behind these effects. In doing so, we want to identify possible sources of bias that are relevant for the use and reuse of these type of data.

METHODS

This paper is based on the authors' experience as users of electronic health records data, as general practitioners, health informatics experts, and health services researchers. It is a product of the discussions they had during the Translational Research and Patient Safety in Europe (TRANSFoRm) project, which was funded by the European Commission and sought to develop, pilot, and evaluate a core information architecture for the learning health system in Europe, based on primary care electronic health records.

RESULTS

We first describe the different stages in the processing of electronic health record data, as well as the different purposes for which these data are used. Given the different data processing steps and purposes, we then discuss the possible mechanisms for each individual data processing step that can generate biased outcomes. We identified 13 possible sources of bias. Four of them are related to the organization of a health care system, whereas some are of a more technical nature.

CONCLUSIONS

There are a substantial number of possible sources of bias; very little is known about the size and direction of their impact. However, anyone that uses or reuses data that were recorded as part of the health care process (such as researchers and clinicians) should be aware of the associated data collection process and environmental influences that can affect the quality of the data. Our stepwise, actor- and purpose-oriented approach may help to identify these possible sources of bias. Unless data quality issues are better understood and unless adequate controls are embedded throughout the data lifecycle, data-driven health care will not live up to its expectations. We need a data quality research agenda to devise the appropriate instruments needed to assess the magnitude of each of the possible sources of bias, and then start measuring their impact. The possible sources of bias described in this paper serve as a starting point for this research agenda.

Acceptance and barriers pertaining to a general practice decision support system for multiple clinical conditions: A mixed methods evaluation

Background

Many studies have investigated the use of clinical decision support systems as a means to improve care, but have thus far failed to show significant effects on patient-related outcomes. We developed a clinical decision support system that attempted to address issues that were identified in these studies. The system was implemented in Dutch general practice and was designed to be both unobtrusive and to respond in real time. Despite our efforts, usage of the system was low. In the current study we perform a mixed methods evaluation to identify remediable barriers which led to disappointing usage rates for our system.

Methods

A mixed methods evaluation employing an online questionnaire and focus group. The focus group was organized to clarify free text comments and receive more detailed feedback from general practitioners. Topics consisted of items based on results from the survey and additional open questions.

Results

The response rate for the questionnaire was 94%. Results from the questionnaire and focus group can be summarized as follows: The system was perceived as interruptive, despite its design. Participants felt that there were too many recommendations and that the relevance of the recommendations varied. Demographic based recommendations (e.g. age) were often irrelevant, while specific risk-based recommendations (e.g. diagnosis) were more relevant. The other main barrier to use was lack of time during the patient visit.

Conclusion

These results are likely to be useful to other researchers who are attempting to address the problems of interruption and alert fatigue in decision support.

Learning health systems need to bridge the ‘two cultures’ of clinical informatics and data science

Background

UK health research policy and plans for population health management are predicated upon transformative knowledge discovery from operational ‘Big Data’. Learning health systems require not only data, but feedback loops of knowledge into changed practice. This depends on knowledge management and application, which in turn depends upon effective system design and implementation. Biomedical informatics is the interdisciplinary field at the intersection of health science, social science and information science and technology that spans this entire scope.

Issues

In the UK, the separate worlds of health data science (bioinformatics, ‘Big Data’) and effective healthcare system design and implementation (clinical informatics, ‘Digital Health’) have operated as ‘two cultures’. Much National Health Service and social care data is of very poor quality. Substantial research funding is wasted on ‘data cleansing’ or by producing very weak evidence. There is not yet a sufficiently powerful professional community or evidence base of best practice to influence the practitioner community or the digital health industry.

Recommendation

The UK needs increased clinical informatics research and education capacity and capability at much greater scale and ambition to be able to meet policy expectations, address the fundamental gaps in the discipline’s evidence base and mitigate the absence of regulation. Independent evaluation of digital health interventions should be the norm, not the exception.

Conclusions

Policy makers and research funders need to acknowledge the existing gap between the ‘two cultures’ and recognise that the full social and economic benefits of digital health and data science can only be realised by accepting the interdisciplinary nature of biomedical informatics and supporting a significant expansion of clinical informatics capacity and capability.

Epidemiology of severe anaphylaxis: can we use population-based data to understand anaphylaxis?

PURPOSE OF REVIEW

The observed increase in incidence of allergic disease in many regions over the past 3 decades has intensified interest in understanding the epidemiology of severe allergic reactions. We discuss the issues in collecting and interpreting these data and highlight current deficiencies in the current methods of data gathering.

RECENT FINDINGS

Anaphylaxis, as measured by hospital admission rates, is not uncommon and has increased in the United Kingdom, the United States, Canada, and Australia over the last 10-20 years. All large datasets are hampered by a large proportion of uncoded, 'unspecified' causes of anaphylaxis. Fatal anaphylaxis remains a rare event, but appears to be increasing for medication in Australia, Canada, and the United States. The rate of fatal food anaphylaxis is stable in the United Kingdom and the United States, but has increased in Australia. The age distribution for fatal food anaphylaxis is different to other causes, with data suggesting an age-related predisposition to fatal outcomes in teenagers and adults to the fourth decade of life.

SUMMARY

The increasing rates of food and medication allergy (the latter exacerbated by an ageing population) has significant implications for future fatality trends. An improved ability to accurately gather and analyse population-level anaphylaxis data in a harmonized fashion is required, so as to ultimately minimize risk and improve management.

Defining asthma and assessing asthma outcomes using electronic health record data: a systematic scoping review

There is currently no consensus on approaches to defining asthma or assessing asthma outcomes using electronic health record-derived data. We explored these approaches in the recent literature and examined the clarity of reporting.We systematically searched for asthma-related articles published between January 1, 2014 and December 31, 2015, extracted the algorithms used to identify asthma patients and assess severity, control and exacerbations, and examined how the validity of these outcomes was justified.From 113 eligible articles, we found significant heterogeneity in the algorithms used to define asthma (n=66 different algorithms), severity (n=18), control (n=9) and exacerbations (n=24). For the majority of algorithms (n=106), validity was not justified. In the remaining cases, approaches ranged from using algorithms validated in the same databases to using nonvalidated algorithms that were based on clinical judgement or clinical guidelines. The implementation of these algorithms was suboptimally described overall.Although electronic health record-derived data are now widely used to study asthma, the approaches being used are significantly varied and are often underdescribed, rendering it difficult to assess the validity of studies and compare their findings. Given the substantial growth in this body of literature, it is crucial that scientific consensus is reached on the underlying definitions and algorithms.