Medical or clinical algorithms are types of software solutions that combine and analyse a variety of data to support different kinds of decision-making:
Physicians and other healthcare providers may use it as an aid to translate complex, diverse clinical data into actionable insights for patient care decisions
Researchers may use it to help to identify patients who may best benefit from participating in clinical studies
Healthcare leaders may also use simple operational algorithms to improve efficiencies in workflows and delivery of care
Medical algorithms can range in terms of complexity from simple decision trees to clinical calculators (e.g., BMI)1 to risk prediction tools (e.g., tools that risk or stratify patients at high risk at different stages of disease)2. It even can include complex machine learning/AI-based algorithms (e.g., identifying people at risk of colon cancer)3. Many of these are used widely in healthcare and may have applications in remote patient care settings already today.
Every day, healthcare providers are faced with a wide array of increasingly complex decisions. As healthcare data is set to grow4, physicians need reliable, trustworthy tools to evaluate the growing pool of diverse data and information. This is why algorithms can provide physicians with a holistic perspective to patient care and play a role at every step of the patient journey.
Screening: Algorithms show great potential for use in the identification of early disease in otherwise healthy populations, i.e., individuals that show no symptoms. Algorithms for example may play a role in helping healthcare providers to determine early diagnosis of disease and may have the potential to improve the accuracy and efficiency of early diagnosis.
Diagnosis: Algorithms can support physicians in making accurate diagnoses based on patient data that is otherwise difficult or time-consuming to interpret without the algorithm. They can also assist in standardising medical decisions (e.g., patient triage, early diagnosis, prognosis). As a result, medical algorithms can help improve quality of care delivery and patient safety as well as contribute to reducing the possibility of errors by minimising manual tasks and automating data entry, when possible.
Treatment: Algorithms can also help physicians in evaluating possible treatment options based on an individual patient's condition and medical history or to adjust treatment approach over time as a patient's condition changes.
Monitoring: Algorithms can help in the monitoring of patients' conditions over time, alerting physicians and patients to potential problems or changes that may require attention. Additionally medical algorithms can support new models of care including remote outpatient monitoring.
Research: Because algorithms can be used to analyse large amounts of medical data, researchers are also using them to identify health patterns and trends in population health over time to prepare the systems for future healthcare needs and priorities.
A common misconception remains that the use of medical algorithms may lead to reduced human contact or to treating everyone in the same way. In fact, the opposite may be true. Medical algorithms’ have the capacity to process and analyse massive amounts of data much faster than a human being. So when implemented effectively, medical algorithms may have the potential to free up time and resources (e.g., administration related to data entry, reporting or data analysis) so that physicians and other healthcare providers can focus their time and energy on what is most important: care for patients.
For the promise of medical algorithms to be fulfilled, however, they need to be implemented effectively. Two main things to consider for healthcare providers are practicality and trust.
On the practical side, it is about interoperability across systems and integrating data silos. For medical algorithms to add true value and decision-making support, healthcare providers must be able to seamlessly use them and easily integrate them within their daily workflows.
On the trust side, medical algorithms need to be based on a range of factors including: being comprehensive and reliable, have proven performance and clinical utility based on accurate, up-to-date knowledge base and data sources, and in some cases have received certification and approval by regulatory bodies or healthcare authorities.
The future of healthcare relies on its ability to strengthen the delivery of care and actionable insights that can facilitate new and enhanced healthcare models that increase access and improve care for patients. Digital health technologies, such as medical algorithms, that can potentially enhance clinical decision-making, offer great promise here, while liberating clinicians’ time and energy to focus on taking care of patients.
References
Calculate your body mass index by the National Heart, Lung and Blood Institute [Internet; accessed 2023 May 10]. Available from
Joon-Myoung Kwon, et al. Artificial intelligence for predicting cardiac arrest using electrocardiography. Scand J Trauma Resusc Emerg Med, October 6, 2020.
ColonFlag for identifying people at risk of colorectal cancer. National Institute for Health and Care Excellence [Internet; cited 2018 Apr 12] Available from:
Reinsel D, Gantz J, Rydning J. The Digitization of the World from Edge to Core. IDC White Paper – #US44413318. 2018 Nov.
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