Smart Analytics

Team Zorg Enablers
Published on
Trends | Diagnosis


“The goal is to turn data into information and information into insight”

Carly Fiorina


Smart analytics is an umbrella term for the application of big data and artificial intelligence (AI). Big data, in turn, is an umbrella term for digital datasets so large, complex and subject to change that they are difficult or even impossible to manage with traditional software and/or hardware. AI refers to all technologies that simulate processes associated with autonomous intelligence, such as reasoning, drawing conclusions, interpreting speech and formulating new sentences. AI techniques underlying such learning capabilities (‘machine learning’) include deep learning and representation learning [1-3]. In the Smart Assistants chapter, we introduced the concept of algorithms that rely on natural language processing (NLP), which focuses on the comprehension of spoken or written language by computers [4]. AI learns from existing datasets and uses that knowledge to process information efficiently. Because big data is so complex that only AI is generally able to analyse it, both terms are often used together [5].


Applications & benefits

Healthcare institutions are data-heavy and the amount of clinical data is set to increase in the years ahead. This makes smart analytics a valuable asset for various healthcare institutions. Healthcare applications of smart analytics include prevention, diagnostics and treatment [6]. Applications can support in diagnostics and clinical decision-making, monitoring and coaching of patients and assisting in treatments and surgery. In addition, smart analytics can support in healthcare systems management. With the knowledge gained, healthcare professionals and other stakeholders in the healthcare system can improve the efficiency and effectiveness of diagnoses. In radiology, for example, patterns are revealed by studying huge datasets from CT scans and patient results in order to diagnose with increasing precision [6-9]. Applications that rely on a combination of smart analytics and genomics are on the rise as well. Smart analytics are being applied more broadly than in clinical settings alone. Many apps used by consumers at home, e.g. for self-diagnosis or symptom tracking, are supported by advanced algorithms [6]. Finally, the technology supports the development of new medication in the pharmaceutical industry and accelerates it by software modelling and training. Smart analytics contribute to a higher quality of healthcare, lower costs and improved patient outcomes.



The market value of smart analytics is growing explosively [10]. The global market value of smart analytics in healthcare is expected to grow from $21.1 billion in 2021 to $75.1 billion in 2026 [11]. Drivers include the increasing demand for analytics solutions, technological innovations and improvements to existing procedures [12]. North America has a large share in the market: in 2020 their market share amounted to over 60 percent [11]. This high percentage can be partially ascribed to federal mandates and the increasing pressure to mitigate healthcare costs. Clinical applications currently account for the majority of the market and are expected to remain the fastest growing component of smart analytics [11, 12].


Driving forces

Growing evidence of effectiveness
Increasing pressure on the healthcare system
Growing availability of (medical) data

Inhibiting factors

Increasing emphasis on privacy sensitivity
Ethical considerations
Lack of expertise

The application of big data and AI is associated with a wealth of ethical issues. A lot of data is collected and decisions are made by a system in a type of ‘black box’. Considerations underlying such decisions can be impossible to retrieve. In addition, it is important to avoid abuse and ensure that patient privacy is upheld, since medical files contain personal and confidential information [11]. The trend is growing due to pressure on the healthcare system and the huge potential of smart analytics. The market is seeing more and more applications that rely on AI to improve themselves, which is driving knowledge and expertise on AI.



Smart analytics have the potential to instigate revolutionary developments in healthcare. This applies to all aspects of the healthcare process in addition to diagnostics. Systems become more autonomous and the healthcare process more personal and predictive. Proper implementation of smart analytics contributes to reduced healthcare costs and improved healthcare quality.


  1. Schmidhuber, J., Deep learning in neural networks: an overview, Neural networks : the official journal of the International Neural Network Society, 2015(61): p. 85-117.
  2. LeCun Y, B.Y., Hinton G, Deep learning, Nature, 2015. 521(7553): p. 436-44.
  3. Helm, J.M., Swiergosz, A.M., Haeberle, H.S., Karnuta, J.M., Schaffer, J.L., Krebs, V.E., Spitzer, A.I. & Ramkumar, P.N., Machine Learning and Artificial Intelligence: Definitions, Applications, and Future Directions, Current Reviews in Musculoskeletal Medicine volume, 2020. 13: pp. 69-76.
  4. Olthof, A.W., Ooijen, P.M.A. van, Cornelissen, L.J., Deep Learning-Based Natural Language Processing in Radiology: The Impact of Report Complexity, Disease Prevalence, Dataset Size, and Algorithm Type on Model Performance, Journal of Medical Systems, 2021. 45.
  5. Fagella, D., What is Machine Learning?, 2018
  6. Topol, E.J., High-performance medicine: the convergence of human and artificial intelligence, Nature medicine, 2019. 25(1): p. 44-56.
  7. Yeung BPM, C.P., Application of robotics in gastrointestinal endoscopy: A review, World Journal of Gastroenterology, 2016. 22(5): p. 1811-25.
  8. Qureshi MO, S.R., The Impact of Robotics on Employment and Motivation of Employees in the Service Sector, with Special Reference to Health Care. Safety and Health at Work. Safety and Health at Work, 2014. 5(4): p. 198-202.
  9. Avgousti S, C.E., Panayides AS, Voskarides S, Novales C, Nouaille L, et al., Medical telerobotic systems: current status and future trends, Biomedical engineering online, 2016. 15(1): p. 96.
  10. Conscious Business Insights, The AI Industry Series: Top Healthcare AI Trends To Watch, 2018
  11. Markets and Markets, Healthcare Analytics Market by Type (Descriptive, Prescriptive, Cognitive), Application (Financial, Operational, RCM, Fraud, Clinical), Component (Services, Hardware), Deployment (On-premise, Cloud), End-user (Providers, Payer) – Global Forecast to 2026, 2021 [Available from]
  12. Market Research Future, Healthcare Analytics Market Research Report: By Type (Predictive, Cognitive), Component (Software, Services), Delivery Model (On-Premise), Application (Financial Analytics, Clinical Analytics), End User (Payers, Hospitals)- Forecast till 2027, 2021 [Available from] 13. PRNewswire, Global Big Data in Healthcare Market: Analysis and Forecast, 2017-2025, 2018