5.7 Enablers of data analytics in healthcare Predictive data analytics

To overcome unstructured clean data entered into various software programs sometimes by unskilled staff, predictive medical taxonomy can be used to improve data entry quality and standardise medical terminology across various data platforms (Dubey et al., 2019).

Standardised data entry platforms

Health organisations need to leverage from available AI and machine learning software to ensure patient data is entered in a standardised format and terminology in an electronic platform that is easily retrievable and streamlined for future integration with other software.

Data governance

Healthcare organisations must provide leadership and create a digital strategy vision for their leaders and staff to follow (Reddy et al., 2022). To improve efficiency and reduce data analysis cost, organisations in the same jurisdictions could streamline their data governance principles to enhance data quality and standardise formats (Reddy et al., 2022). This will further facilitate its integration into all software across the same organisations.

Cloud data

Healthcare organisations should consider cloud data storage solutions to store sensitive data (Xu et al., 2019). Cloud servers are known for their improved multilevel security protocols for sensitive data. One of the first adopters of this storage methodology was Google, which began a database storage system back in 2003 (Ghemawat et al., 2003).

Data visualisation

Electronic dashboards are now used across many industries, including in the health sector, to turn unstructured data into interactive graphs and charts that can be understood by all levels of staff and patients regardless of background or education (Zhao-hong et al., 2018). Well-designed dashboards can often identify hidden patterns and correlations in datasets that are not easily identified unless presented as infographics or specialised maps (Zhao-hong et al., 2018).