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    Clinical Decision Support Systems

    January 31 2022


    Reading time 12 minutes

    A great number of tasks that physician is encountered with in their practice require quick and accurate response.  Diagnosis and treatment must take into account:

    examination data
    patient’s individual characteristics
    results of laboratory and instrumental methods of analysis.

    Routine operations, such as medical paperwork, monitoring of the patients’ condition, and control over the observation of prescriptions, create an additional workload for specialists.

    The emergence of new rules of medical treatment, including clinical recommendations, standards and protocols, complicate the situation. Clinicians do not always have an opportunity to update their knowledge  due to the increasing volume of specialized information and its sources (1).

    In this context, it is important to ensure the timeliness and safety of clinical interventions: to detect a disease in time and start the correct treatment. Partial automation of diagnosis and treatment and specialist information support are aimed at reducing the burden on the physician.

    Artificial intelligence implemented in a Clinical Decision Support System (CDSS) can assist physicians in solving clinical problems


    What is a Clinical Decision Support System?

    CDSS is a computer program that provides a physician with patient’s clinical data and assist them in making decisions. Immediate information support allows for the developing the right medical tactics. Specialist’s clinical thinking combines with artificial intelligence to raise the quality of diagnosis and treatment (2).

    Artificial intelligence in the CDSS is applied by means of crisp logic and fuzzy logic, machine learning methods and natural language processing (3). Computer vision and deep learning help to recognize medical images and interpret them (4).

    As new data arrives, AI is able to learn on its own and improve the accuracy of decision-making (3).


    The CDSS Tasks

    Reference and information support

    The service provides a specialist with the latest clinical recommendations and protocols. Information about medicines helps to refine certain aspects of therapy (5).

    Assistance with medical records

    The system sorts and accounts electronic medical records. The patient’s disease is coded according to the International Classification of Diseases Version 10 (ICD-10). When making diagnosis, a physician chooses relevant code (6).

    Identification of the degree and severity of the disease

    The algorithm assesses patient’s condition using machine learning classifications. The physician receives a conclusion about the risk group as an output (7).

    Warning signals generation

    The CDSS is able to reveal hidden patterns that a physician may not notice. The algorithms warn about possible complications, for example, elevated glucose level in diabetes mellitus (9), infection in the postoperative period (9), decompensation of the disease (10).

    Assistance in diagnosis

    AI is able to carry out a “consultation” based on the input: the user’s symptoms and complaints. The system provides a diagnosis that serves as a guide for a physician (11). Another direction is a diagnostic visualization. The CDSS recognizes medical images and highlights suspicious areas (2).

    Treatment optimization

    The CDSS helps to find the correct treatment based on the electronic medical records data (10), to prescribe the optimal dosage of medicines (12), predict the length of hospital stay and monitor the therapy (13).

    The attainment of economic efficiency

    The introduction of AI helps to direct resources of a medical organization properly. This is achieved due to the efficient use of diagnostic equipment (14) and electronic documentation (10).



    Diagnosis with the use of the CDSS is based on an organic combination of the professional skills of physicians and technical capabilities of AI. AI’s conclusion has a probabilistic nature and serves as a guide for the clinician. The system automates routine processes in medicine.

    Google supported the development of an algorithm for automatic detection of diabetic retinopathy, retinal damage in diabetes mellitus. Training data included 128,175 images of retina (15).

    Sets for clinical tests consisted of 9,963 and 1,748 images respectively. Ophthalmologists evaluated the quality of the image and the presence of visual pathology on it. Their conclusions were compared with those of the neural network. According to the researchers, the sensitivity of the sets was 97.5% and 96.1% (15).

    The Russian service Care Mentor AI uses AI to enhance the accuracy of the radiologist’s conclusions. The expert system analyses the results of radiology examinations to detect signs of thoracic organs diseases (16).

    Care Mentor AI Service makes it possible to (16):

    detect a damage in lung tissue, presence of a foreign body or a cavity with liquid level

    identify areas of pathological changes

    calculate the size of suspicious formations

    formulate a final conclusion.

    In Russia the SberMedAI team is implementing a multi-component medical decision-making support system:

    TOP-3: analyzes data from the initial appointment and provides 3 most probable diagnosis under the ICD-10

    CT Stroke: searches for areas of acute cerebral circulation disorder on tomograms

    CT Lungs: analyzes x-rays of the thoracic organs to look for the signs of viral pneumonia, including caused by COVID-19 or cancer. The service highlights affected areas and minimal nodular densities in the lung tissue.

    Mammography: identifies suspicious masses in the mammary gland

    Short ECG: processes the ECG results with online display

    AI Resp mobile app: studies acoustic characteristics in respiratory system diseases

    AI Skin mobile app: identifies skin rashes by photo.


    Predictive Analytics

    In order to choose treatment, clinicians evaluate the degree of the disease assigning a particular group or category to it. They use generally accepted international classifications as a guideline.

    Clinical Decision Support System accelerates this task. AI uses input data about the patient’s health to do it, including gender, age, previous diseases, symptoms and laboratory measures (10).

    The researchers of the Cancer Institute of Bari Named After John Paul II in Italy have published preliminary results on the prediction of breast cancer recurrence. The used machine learning-based model. The results of examination and treatment of 256 research center patients were used as the input. The probability of cancer recurrence was assessed in 5 and 10 year terms after the diagnosis (17).

    The Italian researchers highlighted the efficiency of the AI-based auxiliary tool: prediction accuracy was 77.5% and 80.39%, and sensitivity was 92.31% and 95.83% for 5 and 10 year terms respectively (17).


    Assistance in Prescribing and Monitoring Treatment

    The CDSS information support can help a physician to make reasonable decisions when choosing therapy (5):

     to see recommended dosages

     to check possible contraindications and unwanted medicine interactions

     to look for cheaper generics

     to make sure that the prescribed treatment is correct and safe.

    German scientists from the University Hospital of Mannheim compared the decisions made by 77 physicians and 89 medical students using various sources of information. The participants were asked to prescribe antibiotic therapy for upper urinary tract infections. The sources of information included Internet, guidebooks and CDSS . The system provided the user with recommendations taking into account patient’s individual features (18).

    The scientists noted that only 27.1% of participants made the correct diagnosis,  while only 19.4% managed to prescribe treatment in accordance with the standards. This shows the necessity of informational support of medical specialists (18).

    The participants whose decisions were based on AI showed the best results: 57.1% and 40.5% among those who made the correct diagnosis and prescribed the right treatment respectively (18).

    In Russia there are services to improve the quality of pharmacotherapy. The Electronic Clinical Pharmacologist system provides a physician with up-to-date information on medicines – State Drug Register and clinical recommendations (19).


    Monitoring of patients’ condition

    The CDSS facilitates the evaluation of qualitative and quantitative measures of patients’ health in dynamics. The use of AI allows a physician to concentrate on each stage of the disease. Control of the individual features is aimed at timely correction of therapeutic and diagnostic measures.

    A publication in The Lancet  by British scientists describes a model that monitors patients’ condition. The Oracle algorithm makes predictions about the results of the psychotherapy. To do it, the Oracle:

     uses data from patients’ questionnaires;
     evaluates depression and anxiety symptoms during each psychotherapy session
     updates prediction of the model after each new session (20).

    High rates of prediction accuracy allowed the authors to consider the Oracle applications in medicine. The algorithm can help a physician to identify patients at risk already at the first sessions (20).

    The American researchers from the Duke University in North Carolina have developed machine learning models to evaluate postoperative risks of complications and mortality. The algorithm was processing a large set of clinical and surgical patient data. The analysis covered 37 million clinical cases and involved 194 indications (20).

    According to the authors, the created model managed to identify patients with high risks after surgery with sensitivity and specificity of 76% (21).

    System Efficiency

    Both domestic and foreign research reviews show the active development of intellectual systems. The effectiveness of the AI use is under study. However, even now technological solutions  are yielding the first positive results in diagnosis and treatment (2, 22-24).

    At this stage we can identify the following aspects of the CDSS use (25):

    • High computer literacy level.Knowing the basics of bioinformatics will help physicians take full advantage of medical technology.
     Transparent decisions. Big data processing methods may not be clear to a user. A clinician needs to know the general principles of how the technology works: how the AI came to this or that conclusion and which performance characteristics and limits the system has.
     Ethics. Algorithmic bias may make a decision based not on the patient’s  features, but on mathematically calculated use. A physician follows the non-maleficence principle and cares about each human life. A specialist take into account the patient’s life situation and preferences in the choice of treatment.

    The CDSS is becoming a highly developed medical assistant, but the key role remains with the human physician. Technologies help physician to save time for their patient. Paying attention to personal conversation, building trust, and becoming partners in the fight against the disease are the key to successful treatment.




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    9. Thara Tunthanathip, Sakchai Sae-heng, Thakul Oearsakul et al. Machine Learning Applications for the Prediction of Surgical Site Infection in Neurological Operations // Neurosurg Focus. 2019. DOI:

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    15. Varun Gulshan, Lily Peng, Marc Coram et al Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs // JAMA. 2016; 316(22): 2402-2410. DOI:

    16. Care Mentor AI: [Online]. URL: (accessed: 26/12/2021).

    17. Raffaella Massafra, Agnese Latorre, Annarita Fanizzi et al. A Clinical Decision Support System for Predicting Invasive Breast Cancer Recurrence: Preliminary Results // Front Oncol. 2021 Mar 11;11:576007. DOI:

    18. M. Neugebauer, M. Ebert, R. Vogelmann. A Clinical Decision Support System Improves Antibiotic Therapy for Upper Urinary Tract Infection in a Randomized Single-Blinded Study // BMC Health Services Research. 2020; 20,185. DOI:

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    21. Kristin M. Corey, Sehj Kashyap, Elizabeth Lorenzi et al. Development and Validation of Machine Learning Models to Identify High-Risk Surgical Patients Using Automatically Curated Electronic Health Record Data (Pythia): A Retrospective, Single-Site Study // PLoS Med 15(11): e1002701. DOI:

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