JMIR Publications

ABSTRACT

Background:

Although the potential of big data analytics for healthcare is well recognized, evidence is lacking its effects on people's health.

Objective:

To assess the effects of the use of big data analytics on people’s health, based on the health indicators and core priorities in the WHO General Programme of Work 2019/2023 and the European Programme of Work (EPW), approved and adopted by its Member States, in addition to SARS-CoV-2 related studies. Furthermore, to review the most relevant challenges and opportunities of these tools with people’s health.

Methods:

Six databases (MEDLINE, Embase, Cochrane Database of Systematic Reviews via Cochrane Library, Web of Science, Scopus and Epistemonikos) were searched from inception date to September 21, 2020. Systematic reviews assessing the effects of big data analytics in the health indicators were included. Two authors independently performed screening, selection, data extraction, and quality assessment using AMSTAR-2 checklist.

Results:

The literature search initially yielded 185 records, 35 of which met the inclusion criteria, involving more than 5,000,000 patients. Most included studies used patient’s data from electronic health records, hospital information systems, patient-private databases and imaging datasets, and involved the use of big data analytics in noncommunicable diseases. With regards to the classification according to health indicators and core priorities prioritized within the WHO General Programme of Work 2019/2023 and the EPW 2020/2025, “probability of dying from any of cardiovascular, cancer, diabetes or chronic renal disease” and to “suicide mortality rate” were the most commonly assessed. Big data analytics has shown moderate to high accuracy for the diagnosis and prediction of complications of diabetes mellitus as well as to the diagnosis and diseases’ classification of mental disorders, prediction of suicide attempts and behaviors, and to diagnose, treat and predict important clinical outcomes of several chronic diseases. Confidence in the results of 25 reviews were rated as “critically low,” seven as “low,” and three as “moderate”, The most frequently identified challenges were establishment of a well-designed and structured data source and a secure, transparent, and standardized database for patients’ data.

Conclusions:

Although the overall quality of included studies is limited, big data analytics has shown moderate to high accuracy for diagnosis of certain diseases, improvement in managing chronic diseases, and support for prompt and real-time analyses of large sets of varied input data to diagnose and predict disease outcomes.