Methodological research priorities for data sciences: Report from The International Methodology Consortium for Coded Health Information (IMECCHI) IJPDS (2017) Issue 1, Vol 1:367 Proceedings of the IPDLN Conference (August 2016)

Main Article Content

Rachel Jolley
Danielle Southern
Hude Quan
William Ghali
Bernard Burnand

Abstract

ABSTRACT


Objectives
The vast amount of data produced by healthcare systems both structured and unstructured, termed ‘Big Data’ have the potential to improve the quality of healthcare through supporting a wide range of medical and healthcare functions, including clinical decision support, disease surveillance, and population health management. As the field of big data in healthcare is rapidly expanding, methodology to understand and analyze thereby enhancing and optimizing the use of this data is needed. We present priorities determined for future work in this area.


Approach
An international collaboration of health services researchers who aim to promote the methodological development and use of coded health information to promote quality of care and quality health policy decisions known as IMECCHI –proposes areas of development and future priorities for use of big data in healthcare. Thematic areas were determined through discussion of potential projects related to the use and evaluation of both structured /codeable and unstructured health information, during a recent meeting in October 2015


Results
Several themes were identified. The top priorities included: 1) electronic medical record data exploration and utilization; 2) developing common data models and multimodal /multi-source databases from disparate sources development; 3) data quality assessment including developing indicators, automated logic checks and international comparisons; 4) the translation of ICD-10 to ICD-11 through field-testing 5) Exploration of non-physician produced/coded data; and 6) Patient safety and quality measure development.


Conclusions
A list of expert views on critical international priorities for future methodological research relating to big data in healthcare were determined. The consortium's members welcome contacts from investigators involved in research using health data, especially in cross-jurisdictional collaborative studies.

Objectives

The prevalence of depression is 2 to 3 times higher in individuals with comorbid medical condition (CMC) than in the general population. When untreated, depression results in increased mortality, higher health care costs, greater functional disability, decreased quality of life and lower adherence to treatment regimens for the CMC. Currently, studies that examined whether depression care in those with CMC is better or worse compared to those with depression alone show inconsistent results. Furthermore, studies that compare depression care in those with specific CMCs are scarce. Improving knowledge base in this area will enable health care systems to better allocate limited resources to clinical population that need them most.

Aims

1) To estimate disparities in depression care in those with one or more CMCs, and 2) to examine if depression care patterns in those with CMCs have been impacted by recent provincial policies.

Method

We retrospectively examined data from physician claims, hospital separations, vital statistics, and insurance plan registries. Using this linked data, we identified dynamic cohorts of individuals with depression and CMCs in 2005 and 2012, based on the earliest date of depression diagnosis. Each cohort had exactly 12 months of lookback period for case ascertainment and 12 months of follow-up for tracking depression care patterns. The following indicators were tracked: 1) receipt of any psychological therapy, 2) receipt of any antidepressants (AD), 3) receipt of any depression treatment, 4) number of GP visits, 5) GP continuity of care index for all visits, 6) GP continuity of care for mental health visits, 6) counts of psychological therapy sessions, 7) proportion of days covered for AD, and 8) continuous medication gap for AD. Disparities in depression care were examined used generalized linear regression models.

Results and Conclusions

Use of depression care in individuals with one or more CMC were higher across most indicators we examined except for AD initiation and GP visits for mental health reasons. In specific CMCs like cerebrovascular disease and diabetes, depression care in some areas were lower. Overall, depression care patterns seemed unchanged after the introduction of relevant provincial policies, although improvements over time appeared to have been made in certain areas.

Article Details

How to Cite
Jolley, R., Southern, D., Quan, H., Ghali, W. and Burnand, B. (2017) “Methodological research priorities for data sciences: Report from The International Methodology Consortium for Coded Health Information (IMECCHI): IJPDS (2017) Issue 1, Vol 1:367 Proceedings of the IPDLN Conference (August 2016)”, International Journal of Population Data Science, 1(1). doi: 10.23889/ijpds.v1i1.389.

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