Consumer Health Informatics (CHI)

Consumer Health Informatics (CHI)

• Health information systems (HISs) are designed for patients (consumers).
• CHI empowers patients with tools and information to manage their health.
• The term "Bringing the Patient into the Loop" emerged at a 1993 conference.
• The Ottawa Decision Support Framework & IPDAS guide development.
• The World Wide Web (1989) enabled vast access to health information.
• CHI encourages self-efficacy and patient empowerment.
• An example is researching treatments for diabetes online.
• Another example is a pregnant woman using an app to monitor her symptoms.

Types of CHI Communication

• CHI interactions fall into three categories: Consumer-to-Information, Consumer-to-Consumer, and Consumer-to-Professional.

Consumer-to-Information

• Focuses on individuals seeking health knowledge via digital tools.
• It prepares patients for doctor visits or to understand diagnoses.
• Searching "symptoms of high blood pressure" on WebMD exemplifies this.

Consumer-to-Consumer

• Involves people sharing experiences and support with each other.
• It often occurs in forums, support groups, or social media platforms.
• Breast cancer patients sharing recovery tips in a Facebook group is an example.

Consumer-to-Professional

• This involves direct communication between patients and healthcare providers.
• It can include messages, video calls, or health portals.
• Messaging a doctor through a patient portal to ask about lab results is an example.

Consumer Decision Support Systems (DSS)

• These are digital tools that help patients in making informed health decisions.
• DSS presents options, risks, and benefits of different health related actions.
• A 2013 study reviewing 3,000 treatments found 50% lacked evidence & only 11% were beneficial.
• The study shows the need for reliable decision-making tools.
• Old tools included books, pamphlets, and videos.
• Modern tools are websites, apps, and interactive decision aids.
• DIPEx shares real patient stories explaining conditions and treatments.
• Mayo Clinic's website explains pros/cons of different treatments.
• Canada Health Infoway is developing e-appointments and e-visits.
• DSS improves decision quality, encourages patient involvement, and supports underserved populations.
• Challenges include tech safety risks, psychological influences, and potential for misinformation.

Virtual Communities (VCs)

• VCs are online spaces where people with shared health concerns connect and support each other.
• WELL (Whole Earth 'Lectronic Link) was an early 1980s discussion platform.
• CHESS (Comprehensive Health Enhancement Support System) supports people with HIV or cancer.
• Mindfulness Virtual Community (MVC) supports mental health through online tools, especially for veterans/Indigenous populations.
• Benefits include building trust, promoting emotional support, and reducing isolation.

Mobile Health (mHealth)

• mHealth uses smartphones and wireless tech to support health care.
• In Canada, 73% of Canadians have smartphones.
• Canada has some of the highest mobile speeds globally.
• 97% of Canadians have LTE network access.
• WelTel is an SMS-based program reminding HIV patients to take meds and helps with follow-ups.
• The Ontario Lung Association App helps asthma patients track symptoms and medication.
• Advantages include instant care access, remote monitoring, and encouraged appointment follow-through,
• Challenges include lack of scientific validation, privacy concerns, and potential for false results.

Wearable and Personal Health Devices

• Wearable devices are electronic devices worn to collect health data.
• The Body Area Network (BAN) includes sensors monitoring heart rate and blood pressure. -BAN also monitors falls (fall sensors), walking patterns (gait sensors), and GPS location.
• Wearables are mainly for chronic disease management and elderly care.
• Fitbits track daily steps and heart rate, sending data to a health app.
• Canadian certified wearable solutions provide coaching, videos, and tracking for people with chronic diseases.

Personal Health Records (PHRs)

• PHRs are electronic systems where patients store, access, and control their health information.
• Patients decide who sees their records, track medications, and update information.
• RelayHealth PHR (Nova Scotia) served 30 doctors and 5,000+ patients in 2014.
• Services include booking appointments, secure messaging, and accessing test results.
• The main challenge is not being fully integrated with all doctor or hospital systems.

Patient Portals

• Patient Portals are web-based platforms allowing patients access to parts of their medical record and interact with healthcare providers.
• Key functions include e-booking (appointments), e-visits (online consultations), and e-prescription renewals.
• Patients can also view/update personal info.
• The purpose is to empower patients to manage their health.

Public Health Informatics (PHI)

• PHI applies information & computer technology to improve public health practices, research, education & surveillance.
• PHI focuses on population-level care through systems that manage health data: surveillance, prevention, and intervention.
• Key Principles include a focus on Population Health.
• PHI seeks to protect the entire communities health by contrast with traditional healthcare.
• PHI is prevention-focused to prevent disease and promote safety.
• It involves multiple sectors, like food safety, environmental monitoring, and workplace safety.
• PHI works within government systems to make decisions like issuing food recalls or tracking disease outbreaks.
• Managed by the Public Health Agency of Canada it's mission is to protect the population by acting with other sectors in collaboration.
• PHI acknowledges social determinants of health, such as income, housing, education, employment, and access to healthcare.
• Main Components include Prevention and Health Promotion.
• PHI uses activities include vaccination programs and smoking cessation campaigns.
• Its Environmental and Occupational Health component monitors pollution and unsafe work conditions.
• The component of Surveillance and early warning systems tracks patterns of disease outbreaks.
• The Drug Safety & Pharmacovigilance component ensures medications are safe through testing.
• The Organ and Blood Transplant Systems component tracks donations, legal/ethical use, and recipient outcomes.
• Public Health Surveillance Systems are used to monitor disease patterns and respond to threats.
• Case Surveillance tracks confirmed disease cases.
• Syndromic Surveillance detects early signs through lab reports or symptoms.
• Sentinel Surveillance uses selected locations to monitor specific diseases.
• Behavioral Surveillance tracks risky behaviors.
• Clinical Outcomes Surveillance studies outcomes from illnesses.
• Lab-Based Surveillance monitors lab data on diseases like STIs or waterborne illnesses.
• IDSR integrates multiple data sources for quicker response.
• Canadian Surveillance Programs also exist such as FluWatch, CCDSS, CIPARS, CNISP, CPSP, Infobase, and eIPDSS.
• It collects and analyzes health data across Canada.
• Canada Health Infoway develops and supports health informatics projects.
• Global Public Health Intelligence Network scans global news to detect early signs of disease outbreaks.
• PHI helps prevent diseases, supports health planning, and increases public awareness.
• Challenges include workforce shortages in IT and coordination between systems.

Electronic Health Records (EHR)

• This chapter goes into EHR, EMR, PHR and more concerning technology and health care
• EMR (Electronic Medical Record) is a digital record within one facility.
• EHR (Electronic Health Record) shares comprehensive digital records across multiple institutions/provinces/country.
• PHR (Personal Health Record) is controlled and managed by the patient,
• A national EHR system's success depends on user acceptance, clear benefits, good project management, strong policies, and secure frameworks.
• Canada uses a Hybrid Model for EHR with both central and provincial control.
• Canada Health Infoway sets strategy, standards, and funding.
• EHR systems in Canada is a $1.1 billion endeavor.
• By 2017, over 162,000 healthcare pros were using EHR systems.
• Initially, there was no EHR access for patients (before 2009).
• In 2009, $100 million was invested to create patient portals.
• EHR Solution (EHRS) architecture includes Point of Service, Health Information Access Layer, and Longitudinal Record Service.
• EHR Viewer is used when PoS isn't available, it connects directly to data via HIAL.
• Canada is developing six core EHR systems: client registry, provider registry, diagnostic imaging, drug/medication profiles, and lab test results.
• The 8 main features of EHR systems, as per the IOM Framework, are Health Information & Data.
• Other features include results management, order entry, decision support, electronic communication, patient support & population health.
• The EHR Maturity Model (EMRAM) by HIMSS tracks how advanced an organization is in using EHR.
• Canada's EHR system is evolving due to Canada Health Infoway, investments across provinces, and work on innovation.

Analytics, Machine Learning, and AI for Health

• Metrics: The numbers used to measure something.
• Indicators: tied to goals or targets.
• Dashboards: Show key indicators visually.
• SMART Indicators: Specific, Measurable, Actionable, Relevant, Time-bound.
• Business Intelligence (BI) uses past/current data to understand what's happening.
• Analytics uses math models to analyze trends, make predictions, and suggest actions.
• Healthcare BI combines different hospital systems into a data warehouse for improved care.
• Types of Analytics include Descriptive, Diagnostic, Predictive, and Prescriptive.
• Artificial Intelligence (AI )are computers that act like humans.
• Machine Learning (ML) is a subset of AI that lets computes learn from data.
• Supervised Learning trains the computer using inputs and known outputs.
• Unsupervised Learning provides the computer with data but no answers.
• Machine Learning Tasks in Healthcare are: predicting disease risk, predicting patient outcomes, and grouping similar patients.
• Ethical Challenges in AI and ML include bias, privacy, inequality, and accountability.
• Analytics and AI are powerful tools to improve healthcare.

Privacy, Confidentiality, and Security

• Breaches have affected millions of personal data, making healthcare data protection essential.
• Privacy is your right to control your personal information.
• Confidentiality is the duty of professionals to keep your data private.
• "Need to Know" Principle dictates to only view patient info if they require it for care.
• Patient identity should be hidden in research.
• Explicit consent is when the patient clearly agrees.
• Implied consent is when Patient's actions suggest agreement.
• Canada protects patient data through Privacy Act, PIPEDA, PHIPA, and PHIPAA.
• Data security efforts must focus on confidentiality, availability, and integrity.
• Safeguards to Protect Data comes in the administrative, physical and technical variants.
• Types of Encryption: Symmetric, Asymmetric, and SSL.
• Turn data into a fixed code (digest).
• Logs track who accessed data.
• Use strong passwords, update software, and install antivirus.
• If you visit a doctor, your info is stored in a secure database.

Emerging Trends in Health Informatics

• We create about 2.5 quintillion bytes of data a day.
• Healthcare data is derived from research articles, hospital records, wearable devices, and health apps.
• Big Data Analytics combines math, data management, and computer science to get insights from datasets. -Data Management needs structuring to work.
• Math is used for using facts and models to analyze.
• The power of computer science allows for running models through data visualization.
• Data Mining: Use software to find hidden patterns in large sets of data.
• Data Mining Steps are Problem Definition, Data Preparation, Modeling, and Deployment.
• Neural Networks mimic human brains in diagnosing diseases and predicting patient outcomes.
• Decision Trees visualize choices and outcomes, predicting behaviors like smoking or alcohol use.
• OLAP (Online Analytical Processing) analyzes trends, forecasting future scenarios.
• Big Data Analytics aims to improve the quality of care and reduce any redundant costs.
• Current healthcare is evolving with AI, IoT, and wearables.