Using big data analytics to improve healthcare
Topics: IT competence Data & Business analytics By Leif Jarle Gressgård
Big data analytics (BDA) is increasingly advocated as one of the most important IT innovations for healthcare organizations [1]. Potential benefits include improving quality of care, reducing waste and error, and reducing the cost of care [2]. However, healthcare organizations struggle to gain benefits from their BDA investments [3]. Improving the ability to exploit big data is therefore a central concern for practitioners.
Healthcare is undergoing rapid transformation, of which big data is a driving force [4]. The last years have seen an explosion of new platforms, tools, and methodologies in storing and structuring data elements. Data can be collected from electronic healthcare records, social media, patient summaries, genomic and pharmaceutical data, clinical trials, telemedicine, mobile apps, sensors, and information on well-being, behavior and socio-economic indicators [5].
Data in healthcare is overwhelming not only because of its volume but also because of the diversity of data types and the speed at which it must be managed [6]. Organizations in this sector are hence data rich [7], but also often less data-mature [8]. Practitioners therefore need a better understanding of the managerial, economic, and strategic impact of big data [3], and how to use BDA to generate valuable insight and make better informed decisions.
Recent studies offer several suggestions on how healthcare practitioners can seize the power of BDA. This includes development of IT architecture, linking data resources to strategic planning, improving data governance, and developing skills of employees and managers.
Big data IT architecture
In several research articles, Wang and colleagues [1,3,9,10] discuss different BDA capabilities that bring different benefits to organizations. The results indicate that, to develop important capabilities and exploit big data, healthcare organizations should improve their big data IT architecture. In doing this, three interrelated architectural components (tools and competencies) should be emphasized: (1) data aggregation, (2) data processing, and (3) data visualization.
- Data aggregation: collect heterogeneous clinical data from multiple sources and transform various sources of data into certain data formats.
- Data processing: process all kinds of data and perform appropriate analyses for harvesting insights related to three kinds of questions: 1) “What has happened in the past?” (called descriptive analytics), 2) “What will occur in the future” (called predictive analytics), and “What to do in the future” (called prescriptive analytics).
- Data visualization: Generate outputs, such as various reports, real-time information monitoring, and meaningful business insights to users in the organization.
- Managers should encourage users to leverage outputs such as reports, alerting, KPIs, and interactive visualizations, to discover new ideas and market opportunities, and assess the feasibility of ideas [3]
Strategic alignment
Combined with development of a big data IT architecture, healthcare managers should focus on alignment between the business and analytics strategies, and formation of a decision-making culture. They should also use the knowledge of how BDA capabilities can add business value to build their capability portfolio of BDA according to their immediate and future plans [1,10].
Evidence-based culture
Building BDA capabilities should be combined with efforts to improve evidence-based decision-making and data governance [10]. Evidence-based decision-making refers to an organizational culture of embracing evidence-based management and embedding evidence-based decision-making in the core values and processes of the organization. This means that organizations should
- Use evidence-based insights for the creation of new healthcare service
- Be open to new ideas and approaches that challenge current or future projects on the basis of new insights
- Allow the incorporation of available information within any decision-making process.
Data governance
Data governance is built on IT governance and aims to formulate data rules and policies and provide a vision and guidelines relating to privacy, security, life cycle and ownership of data by aligning the objectives of multiple functions [10]. To implement effective data governance, organizations should [3]
- Formulate the missions of data governance, with clearly focused goals, execution procedures, governance metrics, and performance measures.
- Define a data governance protocol to provide clear guidelines for data availability, criticality, authenticity, sharing, and retention that enable healthcare organizations to harness data effectively from the time it is acquired, stored, analyzed, and finally used. Organizations should emphasize [10]
- Clarifying the role of data as an asset
- Establish the requirements of intended use of data
- Establish the semantics of data so that it is interpretable by the users
- Specify access requirements of data
- Determine the definition, production, retention, and retirement of data
- Review the data the organization gathers within all the units and define its value.
- Emphasize integration of information across systems and data sources into big data analytics frameworks
Healthcare organization should also consider establishing a data governance committee for managing the availability, usability, integrity, and security of the organization's data.
Organizational agility
Development of BDA capabilities should be combined with development abilities to adapt to ongoing changes in the business processes and functional activities of the organization [10]. To achieve this, organizations should
- Generate, disseminate, and respond to market intelligence about customer needs on a frequent basis
- Develop adequate routines to acquire, assimilate, transform, and exploit existing resources to generate new knowledge
- Improve the effectiveness in managing dependencies among resources and tasks to synchronize activities
- Improve the effectiveness in integrating disparate employees’ inputs through heedful contribution, representation, and interrelation into their group
- Form and execute novel solutions by reconfiguring available resources.
Competence/skill development
Improving the technical and business skills of analytics personnel is important [3; 9]. Organizations should therefore
- Provide analytical training in areas such as basic statistics, data mining and business intelligence to those employees who will play a critical support role in the new information-rich work environment. Mentoring, cross-functional team-based training and self-study are beneficial training approaches to help employees develop the big data analytical skills they will need.
- Adjust their job selection criteria to recruit personnel with relevant knowledge and analytical skills.
Referanser / kilder
- Exploring the path to big data analytics success in healthcare
Wang, Y. & Hajli, N. (2017). Journal of Business Research, 70, 287-299. - Concurrence of big data analytics and healthcare: A systematic review
Mehta, N. & Pandit, A. (2018). International Journal of Medical Informatics, 114, 57-65. - Big data analytics: Understanding its capabilities and potential benefits for healthcare organizations
Wang, Y., Kung, L. & Byrd, T.A. (2018). Technological Forecasting and Social Change, 126, 3-13. - Applications of business analytics in healthcare.
Ward, M.J., Marsolo, K.A. & Froehle, C.M. (2014). Business Horizons, 57(5), 571-582. - Benefits and challenges of big data in healthcare: An overview of the European initiatives.
Pastorino, R., De Vito, C., Migliara, G., Glocker, K., Binenbaum, I., Ricciardi, W. & Boccia, S. (2019). European Journal of Public Health, 29, Issue Supplement 3, 23-27. - Big data analytics in healthcare: promise and potential
Raghupathi, W. & Raghupathi, V. (2014). Health Information Science and Systems, 2(3). - Big data and AI Executive Survey 2021
Report from NewVantage Partners - Nearly 80% of Healthcare execs investing more in big data, AI
Kent, J. (2019). Article at HealthITAnalytics.com - An integrated big-data analytics-enabled transformation model: Application to health care
Wang, Y., Kung, L., Wang, W.Y.C. & Cegielski, C.G. (2018). Information & Management, 55(1), 64-79. - Leveraging Big Data Analytics to Improve Quality of Care in Healthcare Organizations: A Configurational Perspective
Wang, Y., Kung, L., Gupta, S. & Ozdemir, S. (2019). British Journal of Management, 30(2), 362-388.
Image by Gordon Johnson from Pixabay
Other relevant external resources
Healthcare Big Data and the Promise of Value-Based Care. Article in NEJM Catalyst
18 Examples Of Big Data Analytics In Healthcare That Can Save People. Article at datapine.com
Data governance: Driving value in healthcare. KPMG Report
Lecture: "Introduction to health care data analytics". (Developed by The University of Texas Health Science Center at Houston.)
See also other relevant lectures at the Population Health Youtube-channel.