Medical diagnosis is an intricate process where a physician acquires and analyzes a patient’s medical information to identify their correct illness, which allows the physician to make clinical decisions on which treatments the patient should follow to improve their health. The patient has the highest chance of recovering when their treatment is tailored to their true diagnosis.
Physicians and other healthcare providers often have challenging amounts of information to digest from disparate sources. This challenge can be a liability when making urgent decisions or identifying patients who are at risk, wasting precious time, delaying diagnosis, and—potentially—resulting in poor outcomes.
While physicians already use various techniques such as physical examinations, diagnostic testing, and medical imaging to aid their interpretation of the patient’s condition, John Hopkins reports that inaccurate or delayed diagnosis remains the largest cause of medical errors.
Misdiagnosis can lead to serious health issues and result in patients undergoing unnecessary and expensive treatments—while their undiagnosed condition goes untreated. Studies reviewed by the Agency for Healthcare Research and Quality suggest that thousands of hospitalized patients die every year due to diagnostic errors. Research published in the journal of BMJ Quality & Safety estimates that, annually, 1 in 20 Americans are affected by an inaccurate misdiagnosis, many of which result in permanent damage or death.
In an article about serious misdiagnosis-related harms published in the journal Diagnosis, around 1 in 10 patients with a “Big Three” disease is misdiagnosed, and approximately 50% of those misdiagnosed die or are permanently disabled as a result.
Identifying patients with a higher propensity of suffering from or developing a particular disease or condition and determining the appropriate treatment is key to managing both disease and costs. For physicians, a primary challenge in correctly diagnosing patient conditions is the large amounts of data they need to process to make urgent decisions. Among all its complexities, the diagnostic process constrains physicians with many uncertainties and limited time.
AI can supplement the existing medical diagnostic process your physicians undergo by helping them analyze large amounts of patient level data to discover the statistical probabilities of various health outcomes. This capability can help providers avoid misdiagnosis—and focus on the root cause of a disease rather than its symptoms.
Depending on the physician’s needs and availability of data, AI can be used to augment clinical decision making, to help predict the likelihood of any particular disease, as long as historically labeled data for that disease exists. Top risk drivers—for example, a patient’s symptoms, past diagnosis, personal background, and admission history—can be highlighted so providers can create customized treatments. The AI can be integrated into the clinical workflow within an Electronic Health Record, or decision-support intelligence can be provided through intelligent apps and dashboards.
Primarily, AI helps physicians predict the likelihood that their patients suffer from early indicators of a certain diagnosis such as heart disease or sepsis. By learning from historical medical data and discovering relationships between patients who were correctly diagnosed with heart disease and their associated characteristics, AI will apply its understanding of these patterns to predict the probability that your existing patients have the disease. AI also informs your physician of the top risk drivers behind the predictions so that they can create personalized treatments for each patient. These risk drivers can include a patient’s symptoms, past diagnosis, personal background, admission history, and more.
This information is strictly meant to supplement a physician’s diagnosis, and should in no shape or form be meant to replace their medical expertise. AI simply brings high risk, and potentially otherwise overlooked patients to the attention of physicians for potential diagnosis.
In addition to predicting the probability of a certain diagnosis, AI also helps physicians understand which specific pathways a patient’s disease falls into and how severe it will develop over time. With this foresight, your physician can leverage AI to run simulations that reveal the effectiveness different treatments will have on curbing severe outcomes for every patient’s unique condition. As these predictions can be generated early in the process and updated every time new information comes in, physicians can apply the appropriate treatment as early as possible to prevent adverse consequences, increase the likelihood of a patient’s survival, and decrease the overall costs of care.
Automated machine learning models built using DataRobot can extract deep insights from demographic and historical patient data. This enables physicians and other medical providers to identify at-risk patients using results that can be easily explained to colleagues and other stakeholders. Providers can be alerted that a patient shows early indicators that suggest a specific diagnosis. Predictions made by DataRobot’s models are intended to supplement diagnoses made by physicians and other healthcare providers—and are in no way meant to replace their medical expertise. The intention is to bring in patients who are high risk or patients who might potentially be overlooked to the providers’ attention for possible follow-up.
Payers who are able to predict certain conditions in advance and intervene are more likely to save costs by preventing patients’ health from deteriorating. People are healthier; insurers (payers) are wealthier. For example, it’s far better to predict cancer and pay for preventative treatment than pay for chemotherapy, MRIs, and other procedures down the road.
DataRobot uses AI to enhance disease propensity modeling, with the goal of identifying patients who are at risk, improving outcomes, and decreasing the overall cost of care. By understanding the specific pathways a patient’s disease falls into and how severely it might develop over time, physicians and other medical providers can leverage simulations that reveal the effectiveness of different treatments for heading off severe outcomes. These predictions can be generated early in the process and updated each time new information comes in, helping providers customize treatment to avoid adverse reactions and increase the likelihood of a patient’s recovery.
DataRobot AI Platform is designed to unify data of all types, from all sources, into a single system of record across an organization. It delivers rapid results, with vital transparency, security, and built-in governance.
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