Medical Decision Making

Introduction to Medical Decision Making

• Medical decision making is the process of selecting courses of action in the management of a patient's health condition.
• This involves evaluating evidence, weighing risks and benefits, considering patient preferences, and using clinical experience.
• Medical decision making is crucial for effective, patient-centered care.
• Decision-making skills are essential for healthcare professionals for optimal patient care and evidence-based practice.

Role of Medical Informatics in Decision Making

• Medical informatics is an interdisciplinary field focused on the use of information and technology in healthcare.
• Medical informatics facilitates access to relevant and up-to-date clinical information like electronic health records, clinical guidelines, and research articles.
• Clinical decision support systems (CDSS) can be developed and implemented to assist healthcare professionals in making informed decisions.
• Artificial intelligence (AI) and machine learning (ML) algorithms can analyze data sets, identify patterns, and support diagnostic, therapeutic, and prognostic decisions.
• Tools and methods for analyzing, visualizing, and communicating medical data can support collaborative decision-making among healthcare professionals and patients.

The Decision-Making Process in Healthcare

• The process involves gathering and organizing relevant clinical data like medical history, physical examination findings, and diagnostic test results.
• Identifying patient problems or clinical questions that need addressing is essential.
• Generating a list of possible diagnoses, interventions, or management strategies is needed.
• Each option must be evaluated for potential risks, benefits, and likelihood of success.
• The process incorporates patient preferences, values, and unique circumstances.
• Selecting the appropriate course of action based on evidence, clinical experience, and patient preferences is critical.
• Implementing the chosen intervention and monitoring its effectiveness is necessary.
• The process continuously updates and revises decisions based on patient response and clinical outcomes.

Types of Medical Decisions:

• Diagnostic decisions: determining causes of a patient's signs, symptoms, or clinical findings.
• Therapeutic decisions: selecting the appropriate treatment for a patient's condition.
• Prognostic decisions: predicting the likely course and outcome of a patient's condition.

Diagnostic Decisions

• Diagnostic decisions involve determining the cause of a patient's signs, symptoms, or clinical findings.
• This process includes collecting relevant clinical information through patient history, physical examination, and diagnostic tests.
• A differential diagnosis is formulated, generating a list of possible diagnoses based on the collected information.
• The differential diagnosis is refined with additional tests or consultations to narrow the list.
• A definitive diagnosis is established by healthcare professionals, serving as the basis for management decisions.

Diagnostic Decisions - How Medical Informatics Assists: Bacterial Meningitis Example

• Electronic Health Records (EHRs): Help healthcare providers access a patient's medical history, previous test results, aiding in differential diagnosis.
• Clinical Decision Support Systems (CDSS): Provide evidence-based guidance and diagnostic algorithms, helping clinicians prioritize potential diagnoses.
• Artificial Intelligence (AI) and Machine Learning (ML): Analyze datasets, identify patterns, and develop predictive models, assisting with diagnostic decision-making.
• Telemedicine and Remote Consultations: Facilitate consultations with specialists, allowing for quicker, more accurate diagnostic decisions.

Therapeutic Decisions

• Therapeutic decisions involve selecting the appropriate treatment or intervention for a patient's condition.
• Identifying potential treatment options is based on the diagnosis that may include medications, surgery, or lifestyle modifications.
• The risks and benefits of each option is evaluated against the available evidence.
• The patient's values, preferences, and unique circumstances are considered.
• Implementation and monitoring of the selected intervention and patient response is required.

Therapeutic Decisions - How Medical Informatics Assists: Type 2 Diabetes

• Evidence-Based Medicine (EBM) resources: Medical informatics provides easy access to up-to-date clinical guidelines and research articles.
• Clinical Decision Support Systems (CDSS): CDSS offer personalized treatment recommendations based on patient characteristics and can also provide alerts for potential drug interactions.
• Patient Education and Engagement Tools: Medical informatics provides tools to help patients understand their condition and treatment options, including shared decision-making.
• Remote Monitoring and Telehealth: Enables healthcare providers to monitor patient progress, adjust treatments, and provide ongoing support.

Prognostic Decisions

• Prognostic decisions involve predicting the likely course and outcome of a patient's condition, considering disease progression, potential complications, and overall health.
• Gathering relevant clinical data includes collecting necessary test results, clinical findings, and patient history to assess their condition.
• Healthcare professionals evaluate the natural course of the disease and likelihood of complications from the assessed data.
• Based on the gathered data, healthcare professionals estimate the patient's likely outcomes and survival.
• The prognosis is shared with the patient, which may inform further therapeutic or advance care planning decisions.

Prognostic Decisions - How Medical Informatics Assists: Advanced-Stage Lung Cancer Example

• Clinical Data Repositories and Registries: Accessing large databases of patient outcomes can pinpoint factors that influence prognosis for facilitation.
• AI and ML Algorithms: To analyze complex datasets and to develop predictive models for estimating the prognosis based on clinical factors.
• Clinical Decision Support Systems (CDSS): To integrate prognostic information with other clinical data (i.e. treatment options, side effects), informing decisions.
• Communication and Visualization Tools: Medical informatics provides tools and visual aids to effectively communicate prognostic information.

Evidence-Based Medicine (EBM)

• EBM is the conscientious, explicit, and judicious use of current best evidence in making decisions about the care of individual patients.
• The practice integrates clinical expertise with clinical evidence from systematic research, also considering patient values and preferences.
• The key principles of EBM include formulating clear questions, searching for the best evidence, appraising the evidence, and applying it to patient care.
• EBM improves the quality and consistency of healthcare decisions.
• Better patient outcomes, reduced healthcare costs, and more efficient use of resources may arise by practicing EBM.
• Healthcare professionals staying up-to-date with the latest research and advances in their field is done by EBM.

The Hierarchy of Evidence

• The hierarchy of evidence is a ranking system used to evaluate the strength and quality of clinical research studies.
• The following levels are included in the hieararchy from highest to lowest quality
  • Systematic reviews and meta-analysis
  • Randomized controlled trials
  • Cohort studies
  • Case-control studies
  • Cross-sectional studies
  • Case series and case reports
  • Expert opinion, consensus statements, and guidelines
• Higher levels of evidence provide stronger support.

Steps in Practicing EBM

• Healthcare professionals (HCP) should formulate clinical questions (using the PICO format: Patient, Intervention, Comparison, Outcome).
• HCP should find the best available evidence (using databases such as PubMed, Cochrane Library, or guidelines from professional organizations).
• HCP should appraise the evidence for its validity, importance, and applicability (considering study design, risk of bias, and relevance to the patient population).
• HCP should apply the evidence to clinical practice (integrating the evidence with clinical expertise and patient preferences to make informed medical decisions).

EBM Example : Clinical Scenario : 55-year-old Hypertensive Patient

• Use PICO to frame your clinical questions:
  • P (Patient): 55-year-old with newly diagnosed hypertension with no comorbidities
  • I (Intervention): Initial antihypertensive medication (ex. thiazide diuretic)
  • C (Comparison): Other classes of antihypertensive medications (ex. ACE inhibitors, beta-blockers, blockers)
  • O (Outcome): Blood pressure control, reduction in cardiovascular events, and side effect profile.
• Search databases such as PubMed or the Cochrane Library using relevant keywords in order to find the best available evidence.
• Review identified studies and assess their quality and relevance.
• Synthesize the findings from the appraised studies, focusing on blood pressure control, reduction in cardiovascular events, and side effect profiles.
• Base on your appraised evidence, medication is identified for safety and effectiveness, patient preference is taken into account, and the medication is administered with monitoring of affect.

Ways to Integrate Evidence-Based Medicine into Practice

• Develop a habit/ encourage others to formulate questions regarding diagnostic, therapeutic, or prognostic dilemmas.
• Establish how to search databases such as PubMed, Cochrane Library, and guideline repositories
• Acquire the skills to assess the validity, importance, and applicability of research studies using resources.
• Incorporate EBM and CDSS into your practice for guidelines, prediction rules, and risk calculators.
• Regular research in your field will help you stay up-to-date and keep your practice evidence-based.
• Share information and get insight via colleagues and specialists.
• Discuss benefits, risks, and treatment options with patients as partners.
• Always assess outcomes, adjust and improve to refine your practice.

AI and ML in Medical Decisions

• Artificial Intelligence (AI) and Machine Learning (ML) are playing an increasingly important role in medical decision making.
• They have enhanced diagnostic accuracy and optimized treatment selection.
• AI algorithms can analyze medical images like X-rays, MRIs, and CT scans - for patterns.
• Lung nodules suggestive of cancer have been detected in chest CT scans with AI and ML, comparable to those of human radiologists.
• ML models can assist in the prediction of the likelihood of certain diseases like (diabetes, cardiovascular disease) on electronic health record (EHR) data.
• With early intervention and prevention efforts, Al and ML models can optimize drug dosing and personalize treatment plans.
• ML algorithms have been developed to predict the risk of sepsis in ICU patients by analyzing large datasets and facilitating early intervention.
• Personalized prognostic models developed for more accurate estimates.

Patient-Centered Medical Decision Making

• Patient-centered decision making involves patients in healthcare decisions to incorporate their values, preferences, and personal circumstances.
• This shared approach recognizes that patients are experts in their own lives, their input is essential for making an informed decision.
• Healthcare professionals should encourage patients to express their concerns. Provide clear and accessible information, and give emotional support.
• Strategies to Improve Patient-Provider Communication and Patient Decision-Making
  • Establish rapport by being attentive, approachable, and respectful with patients.
  • Communicate complex medical information in a way that patients can understand, avoiding jargon and technical terms.
  • By asking open-ended questions you can encourage patients to share thoughts and feelings.
  • Utilize tools such as brochures, videos, or online resources to help patients understand the potential outcomes, benefits, and risks.
  • Confirm understanding through a clear discussion about their healthcare choices.

Case Study 1 : Sepsis Detection in ICU

• ML algorithms were implemented to predict sepsis
• System identified those at high risk and quickly alerted the healthcare team
• This system showed potential to reduce morbidity with detection and treatment.

Case Study 2 : Personalized Diabetes Management

• AI Driven CDSS analyzes a variety of patient metrics
• The system can recommend treatment modifications through medication adjustments, lifestyle changes and unique needs.

Case Study 3: Telemedicine for Rural Patients with Chronic Conditions

• A Rural patient can now access specialized care through the use of remote technology and access.
• Via video consultations the provider and patient can make needed adjustments with access to the necessary information.