Big data in systemic sclerosis: Great potential for the future

Radić, Mislav; Frech, Tracy

doi:10.1177/2397198320929805

Cited by 3 publications

(2 citation statements)

References 27 publications

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“…There is a powerful role for data gathered from the EHR to develop hypotheses, link data sources, expand analyses and advance SSc care [ 23 ]. To our knowledge, this is the first study to evaluate accuracy of collective SSc M34*ICD-10 codes extracted from a variety of clinical scenarios (billing codes, encounters and problem list) within the EHR and the use of UTP algorithms to refine the cohort based on keywords of organ system involvement including esophageal disease or gastrointestinal symptoms.…”

Section: Discussionmentioning

confidence: 99%

Development and validation of algorithms to build an electronic health record based cohort of patients with systemic sclerosis

et al. 2023

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Objectives To evaluate methods of identifying patients with systemic sclerosis (SSc) using International Classification of Diseases, Tenth Revision (ICD-10) codes (M34*), electronic health record (EHR) databases and organ involvement keywords, that result in a validated cohort comprised of true cases with high disease burden. Methods We retrospectively studied patients in a healthcare system likely to have SSc. Using structured EHR data from January 2016 to June 2021, we identified 955 adult patients with M34* documented 2 or more times during the study period. A random subset of 100 patients was selected to validate the ICD-10 code for its positive predictive value (PPV). The dataset was then divided into a training and validation sets for unstructured text processing (UTP) search algorithms, two of which were created using keywords for Raynaud’s syndrome, and esophageal involvement/symptoms. Results Among 955 patients, the average age was 60. Most patients (84%) were female; 75% of patients were White, and 5.2% were Black. There were approximately 175 patients per year with the code newly documented, overall 24% had an ICD-10 code for esophageal disease, and 13.4% for pulmonary hypertension. The baseline PPV was 78%, which improved to 84% with UTP, identifying 788 patients likely to have SSc. After the ICD-10 code was placed, 63% of patients had a rheumatology office visit. Patients identified by the UTP search algorithm were more likely to have increased healthcare utilization (ICD-10 codes 4 or more times 84.1% vs 61.7%, p < .001), organ involvement (pulmonary hypertension 12.7% vs 6% p = .011) and medication use (mycophenolate use 28.7% vs 11.4%, p < .001) than those identified by the ICD codes alone. Conclusion EHRs can be used to identify patients with SSc. Using unstructured text processing keyword searches for SSc clinical manifestations improved the PPV of ICD-10 codes alone and identified a group of patients most likely to have SSc and increased healthcare needs.

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Section: Discussionmentioning

confidence: 99%

Development and validation of algorithms to build an electronic health record based cohort of patients with systemic sclerosis

et al. 2023

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“…The researcher engaged in the data analysis process finding literature on digitalisation and comparing it to the data. At the point when data collection continued and no new categories were created this was recognised as data saturation, where the analysis of data reveals few, if any new insights (Radic and Frech, 2020).…”

Section: Methodsmentioning

confidence: 99%

Aligning strategy and digitalisation activity as an incremental or radical innovation in family farms

Appleton,

Holt

2023

IJEBR

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PurposeDigitalisation is perceived as a new process that may add value to firms. Current theoretical understanding assumes it should be part of a firm's strategy to respond to multiple pressures in the business environment. This paper explores the occurrence of digitalisation in a rare context, that of the English agricultural industry in the United Kingdom, a place disproportionality filled with family firms. The general understanding of digitalisation in family firm settings remains embryonic. The authors' explorations make theoretical contributions to research at the intersection of rural entrepreneurship, family business and innovation.Design/methodology/approachUtilising a purposive, qualitative approach, primary data was collected from multiple interviews with 28 UK family farms, and secondary data from another 164. Interview transcripts were coded using NVivo, along with secondary data from reports, observations and websites.FindingsThe authors present empirical evidence illustrating how digitalisation manifests incrementally and radically in different types of family farms. The authors present a model that shows the areas of farming that have, and continue to be, digitalised. This increases analytical precision when identifying digitalisation activities that differ depending on the strategy to either scale or diversify. The authors propose that incremental digitalising occurs to a great extent during a scaling strategy, and that radical digitalising occurs to a smaller extent during diversification strategies in family farms.Research limitations/implicationsThis research uses a sample of family-run farms from the UK agricultural sector to explore nuanced elements of digitalisation. It should therefore be explored in other types of family firms located in different sectors and geographies.Practical implicationsThis research is important because family farms are under increasing pressure and have limited financial resources to deal with the digitalisation agenda. Therefore, empirical evidence helps other farms in similar situations. The authors found digitalisation investments, that tend to be capital intensive, only matter for scalers and less so for diversifiers. Family farms can use the model presented as a tool to evaluate their farm. The tool helps them define what to do, and ideate the potential activities that might be digitalised, to feed into their wider strategy.Social implicationsFamily firms, in particular farms, are critical to many economies. The general consenses currently assumes all family firms should digitalise, yet the authors' evidence suggests that this is not the case. It is important to create policies that are sensitive to the needs of different types of businesses, in this case between family firm scalers and diversifiers, instead of simply incentivising digitalisation using a blanket approach usually by offering financial aid. Understanding how digitisation can support (or not) family firm resilience and growth in an effective and efficient manner can have significant benefit to individual firms, and across industries.Originality/valueThe proposed model extends theoretical understanding linking strategy, digitalisation activity and innovation in family farms. It shows that digitalisation is a key building block of scaling strategies, maximising digitalisation to increase efficiency. Yet, diversifying family farms minimise digitalisation, whereby they only digitalise a small amount of the farming activity. This empirical evidence contrasts with the wider narrative that farmers are slower at using new technology. This research found that some are slower because it does not align with their strategy. However, sometimes digitalisation aligns with their strategy during external changes, in which case the diversifiers are quick to act.

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