A recent Johns Hopkins study suggests more than 250,000 people in the United States die every year from medical errors – other studies claim the numbers to be as high as 440,000.
Medical errors are the third-leading cause of death in the United States alone. Human factors including errors in diagnosis, gaps in history taking, and practitioners misinterpreting the initial information data encountered, play a major role.
GP or medical exams are designed to test the application of knowledge in the clinical context rather than just knowledge per se. It is designed to assess how a candidate integrates their applied knowledge and clinical reasoning, when presented with a range of clinical scenarios. It allows a candidate to demonstrate their clinical skills, communication skills and professional attitudes in the context of consultations, patient exams and peer discussions. It is a clinical consulting performance assessment.
Meksi, a specialized technology healthcare company, have identified that the current methods of assessing medical student competencies through case study role plays is entirely manual. It is time consuming, expensive and is not standardized.
That is why Meksi uses simulation techniques in a variety of learning, training and assessment scenarios. The adoption of simulations as a viable learning technique has sparked an evolution both in the teaching of medicine and how trainees and junior doctors develop their essential consultation skills.
With the ever-evolving nature of quality patient care, doctors not only have to master the knowledge and procedural skills, but also the ability to effectively engage with patients, relatives, and other health care providers, while coordinating a variety of patient care activities.
Meksi ensures that students and medical professionals, can prepare themselves efficiently against a pre-determined
benchmark in a simulated consultation environment before encountering them in real-life scenarios.
These simulated consultations provide trainees with the opportunity to learn and re-learn the processes as often as necessary. They are given the opportunity to correct mistakes and refine practices, enabling them to bridge the gap between their theoretical knowledge and practical real-world scenarios.
IBM Watson Artificial Intelligence
Meksi is revolutionizing medical education by maximizing learning outcomes with IBM Watson AI. Its platform allows medical professionals and students, to build competency in areas such as history taking, physical examination, diagnosis, ordering & interpreting investigations, as well as clinical management & communication with patients.
Leveraging the Machine Learning and NLP (Natural Language Processing) capabilities of IBM Watson, the Meksi platform creates a Virtual Patient and Case Study based clinical examination model that allows the assessment of the candidate’s medical knowledge, clinical skills and professional attitudes, for the safe and effective clinical practice of medicine.
The Virtual Patient
At most institutions, medical students learn communication skills through the use of standardised patients (SPs), but this is time and resource expensive. The use of Virtual patients (VPs) however, offer several advantages over SPs and can be used to teach medical students both history taking and communication skills.
This is important because understanding the patient’s actual input is an essential part of any diagnosis and allows for the disambiguating between different context, which itself, can be regarded as the real test.
For example, suppose a patient is suspected of having pneumonia. After three days on antibiotics, the patient hasn’t really improved. The Meksi system, having absorbed thousands of previous cases, enables the doctor with clinical skills, to identify whether the trajectory is normal or unusual, and whether a different medicine or course of action is required.
Asking these specific questions to understand the patient history and context at the right time is critical for successful diagnosis – the Meksi IBM Watson platform allows this to occur.
Meksi has created a virtual patient system leveraging AI driven conversations, to simulate interactive patient scenarios. This helps in reinforcing basic medical knowledge while solving a specific type of medical case with clinical reasoning.
Artificial Intelligence Based Assessment & Scoring Model
Progressive goal setting and feedback loops can help medical students to rapidly improve their skills and capabilities.
The Meksi platform provides this with its assessment model based on three different quotients axis – academic, behavioral, and test taking.
The assessment model evaluates…
- Communication & Rapport – the ability to establish rapport and to communicate effectively with the patient in a pleasant, clear and logical manner, using appropriate communication skills and
- History Taking – the ability to take a relevant and organised history, following appropriate cues and eliciting positive and negative details important to the assessment and management of the
- Physical Examination – the ability to perform an appropriate and systematic examination that is appropriately focused and not overly inclusive. Candidates should be able to detect physical examination findings accurately and interpret them
- Diagnosis – the ability to make an accurate diagnosis based on interpretation of the history, physical examination and
- Management – the ability to manage the issues raised in the case. This may include immediate management (eg emergency measures), short-term management (eg safety-netting for the patient) and long-term management (eg prevention of recurrence), and preventive health.
The medical practitioner’s patient notes are assessed leveraging Natural Language Processing, that interprets techniques which include the extraction of facts, dosage information, plus the complexity of decisions made by the medical practitioner.
The Meksi AI enabled platform, has been developed by a team of the world’s most respected medical, digital and creative minds.
Company: Meksi Website: www.meksi.com
Solution: IBM Watson AI