Pharm Eco Final

(FITB) Precision medicine

(FITB) Precision medicine

PM targets patients' needs based on genetic, phenotypic, or psychosocial characteristics to develop individualized treatment recommendations

Precision medicine (PM) uses patients genetic and molecular profiles to deliver targeted therapies and treatments

PM is a paradigm transitioning clinical care to a personal level

PM programs provide specialized platforms for accessing, designing, and implementing unique treatment plans

Goal is to use all healthcare information available to maximize the possible benefit from treatment a patient may see

(FITB) Equity (Difference between equality and equity)

Equal opportunity, equity is equal outcomes Equality – provision of the same resources/services to everyone Equity – provision of resources based on need, in order to achieve as equal an outcome as possible

Interoperability

Refers to the ability of different information systems, devices and applications to access, exchange, integrate and use data cooperatively in a coordinated manner

Redundancy

A challenge of health informatics databases Overlapping and fragmented information sources Unnecessary cose Databases aim to eliminate redundancies

When robotics being used in healthcare started

1980s

when did 3D printing start

3D printing was first available in 1980 and has grown in popularity

when was HIPAA established

established in 1996

Percentage - american adults health literacy

Only 12% of American adults are health literate and can manage self-care

Artificial intelligence

Refers to computer systems designed to model human cognitive fxn Can conflict w/ pt centered care and make it feel impersonal

PARO

Robotic animal that is example of a socially assistive robot (SAR) that can simulate pet-like interactions; can be employed to automate supervision of those affected by stroke, dementia, age-related disease and other growing populations Assists those w/ cognitive impairments w/ med intake, bathing, dressing, interactivity, companionship

Drug delivery and nanomachines

Most well-developed function of nanomachines Offer potential for microscopic functionality of various systems w/in body Nanomachines interact on small scale w/ body processes and can influence tx plans Different interactions can guide nanomachines through body

Drawbacks or robots in medicine

Damage difficult to reach tissue they are meant to reach and treat Biocompatibility is essential Potential inflammatory responses Risks of SAR → physical harm, potentially damaging attachments, users may personify devices

3D printing

3D printing can create a prosthesis tailored to specific measurements, model organs, tissue scaffolds, and more Drug printing allows drug manufacturing to be more flexible and efficient Layer-by-layer fabrication techniques allows 3D printing to create usable medical tools 3D printing can integrate PM by creating medical components tailored to positive health outcomes for a specific individual 3D drug printing can improve the delivery effectiveness and production of various drugs

CAS-9

A protein involved in CRISPR technology binds to the DNA and cuts it, shutting the targeted gene of Genetic modification

Digital therapeutics and health conditions

Digital therapeutics serves as another technology to provide diagnosis and treatment for various health conditions (includes epilepsy, neurological disorders, and diabetes) They allow patients to access care remotely, and can improve treatment and medication adherence Locations otherwise unserved in terms of mental health care can access treatment options both with a clinician or without

Smart skin adhesive patches

Uses skin to monitor vital physiologic signals via real-time health monitoring systems Continuously measures vital signs like — ECG —Heart rate —Heart rate variability —Respiratory rate —Temperature

Internet of things and proximity layer

The framework describing the utility of the various types of wearables and sensor consists of four basic layers: —The perception layer – sensing systems that collect data —The network layer – data communication and storage —The application layer – healthcare user applications —The proximity layer – health device/sensor proximity Examples include “tele”, “Derma”, “In vivo”

In vivo sensors

Implantable, ingestible, or otherwise invasive sensors that go beyond wearable applications Primarily used in biomedical research and diagnostic medicine Examples – pressure sensors, electronic pills, strip-type glucose monitoring systems, and smart tattoos

Electronic pill

Ingestible digital capsules that pass through the gastrointestinal tract, delivering real-time information to the healthcare professional's data monitoring device Gathers temperature, pH, and pressure information in diagnosing: —stomach-related diseases —Malabsorption disorders —Unexplained bleeding and inflammatory bowel illnesses

Challenges to privacy regulations w/ health data

Privacy = a person’s right to control their own personal information Privacy standards and regulations had developed independently for clinical care compared to research practice prior to the Health Insurance Portability and Accountability Act (HIPAA)

Paper breaches

Paper documents are the most common form of data breach, but network breaches affect more people

HIPAA

Adherance involves —Performing risk analysis —Training employees on HIPAA requirements, procedures, and the potential for harmful digital interaction —Ensuring operating organizations have shared the standards in agreements with any other business associates (defined by USDHHS) —Protecting health information wherever it is stored u Having insurance to deal with cyber incidents Health Insurance Portability and Accountability Act

Phishing tests

Email phishing remains a prevalent issue and is believed that even whentrained, 20% of employees in an organization will fail phishing tests “ an attack that attempts to steal your money, or your identity, by getting you to reveal personal information”

Wearable technology

Wearable technology provides a way for patient health data to be updated more regularly, thus impacting a patient’s diagnosis, treatment, and healthcare management

Benefits w/ genetic testing

learning about ancestry Addressing medical health issues Curiosity

CHI tool

Consumer Healthcare Informatics CHI as a concept revolves around people managing their own healthcare through informatic tools CHI tools have advanced from basic patient portals to advanced mobile apps, EHR, and telehealth

Benefits of Chi

Patients are no longer satisfied with the routine of personal care Tech-savvy patients seek more responsive and convenient care As conventional healthcare costs climb, healthcare systems that want to be more appealing to insurers and third-party payer will integrate CHI tools CHI integration can create different opportunities: —Engage patients —Generate knowledge —Aid clinical decisions —Transmit health information —Improve interaction and support Messaging capabilities through CHI tools allow for active communication between patients and healthcare providers Consumer education Social engagement Self-triage Monitoring

Examples of Chi

Personal health records u Telehealth Mobile health (mHealth) u Precision medicine Consumer genomics

Consumer health informatics

CHI as a concept revolves around people managing their own healthcare through informatic tools CHI analyzes consumer needs and uses strategies to make healthcare information easily accessible Intersection of healthcare delivery and public education CHI can share data surrounding health promotion, treatment, and disease management and prevention

CDSS

Clinical Decision Support Systems → computer based software that empowers physicians, healthcare staff, and pts with key information targeted on specific individuals or situations; integrated within the clinical workflow to air decision-making process Can improve pt safety, assist with clinical management, reduce cost, manage admin functions,a assist ordering tests and procedures, provide diagnostic and pt decision support Background - types → knowledge based systems that fxn via literature and practice based information; non-knowledge based systems that make decisions based on trends and pattern recognition Defined through nature of information delivery or outcomes

Core elements of CDSS design

Data management layer; processing layer; user interface

Types of malware/characteristics to distinguish them

Types of malware → virus, worm, trojan, spyware Characteristics to differentiate → self replication (ability to copy itself), population growth (overall change in malware instances resulting from self replication) and parasitic (whether it requires another piece of code to function)

HIPAA and the EU (EU doesn’t have HIPAA; have another form of regulation)

Compared to the EU’s General Data Protection Regulation (GDPR), HIPAA is regulating information rather narrowly despite the electronic use of information growing For example, GDPR regulates biometrics and genetic data, whereas HIPAA does not

Difference between virus, worm, trojan, spyware

Virus → malicious software the is designed to be spread from one computer to another Worm → malware programs that replicate quickly and spread from one computer to another Trojan → malicious bit of attacking code or software that tricks users into running it willingly by hiding it behind a legitimate program Spyware → Installed on computing device without one’s knowledge to record personal information

Gaps in public health informatics

Public health informatics = multidisciplinary framework incorporating information science, data analytics, and technology to benefit population health instead of focusing just on individuals Gaps → low uptake, multiple systems used in various jurisdictions that don’t communicate with one another (leads to gaps and redundancy); inconsistent and improper use of technology

Core discipline of public health

Behavioral science and health education, biostatistics, epidemiology, environmental health, health admin and policy

Core function of public health

Assessment and surveillance, health promotion, health protection, disease and injury prevention, emergency management

Big data analytics applications

Clinical —Can assist virtual nursing assistants in providing analysis and health recommendations for their pts while they respond remotely —Can help simplify imaging and technology and combine image acquisition and reconstruction techniques to assist in diagnosing illnesses —Can benefit pathology, improving accuracy, precision, and consistency of disease predictions —Can be used in oncology to determine difference between benign and malignant growths Research —ML can make clinical trials quicker and less expensive by using predictive models to identify eligible subjects —Social media analysis can assist in uncovering consumers’ behaviors, preferences, and eventual satisfaction w/ their purchasing experience —Can be used for both pts and providers to extract pt discussions about tx, emotions, medication AEs, ad ratings for their providers —NNs can be used to monitor epidemics and outbreak predictions worldwide Administration —Insurance - personalized care plans, faster claims handling, error detection

Machine learning techniques

Supervised learning Unsupervised learning Reinforcement learning Deep learning → form that uses neural networks

Deep learning regarding neuro networks

Neural networks consist of individual neurons (nodes in system) that each take inputs of data, conduct mathematical computations, and then produce an output Multiple layers of neuros - an input layer, one or more hidden layers, and an output layer

8 V’s of big data

Volume (size of data), velocity (speed of data generation), variety (type of data), veracity (accuracy of data), validity (accuracy of data for intended use), volatility (Age of data), visualization (readability of data), value (desired worth of data)

Factors driving interoperability

Government mandates, desire to increase efficiency, desire to limit paperwork, desire to create real-time communication, higher ROI

EHR implementation

Challenging Epic, Cerner, Meditech are largest providers Differences in functions can provide benefits and challenges to consumers Variables → area of clinical care, hospital size, financial management, etc.

Advantages of EHR over EMR

Equally good health information and data management capabilities Improved results management, added decision support, ease of admin processes, more timely, more accessible by pts

Linked databases

Linking databases can add value to an organizations database Linking databases presents new security challenges and concerns

Peer reviewed literature

Type of data sources

Technical solutions for cyber security

Simple → firewalls to prevent viruses, limited access to track where and when data has been permitted for convenient access, good password management

Complex → active antivirus programs, limited medical device interconnectivity, data encryption

Quantitative data examples

Numerically expressed Test scores, experimental results, surveys, market reports

Who interacts w/ EHRs the most

Electronic Health Records - Digitally stored healthcare record (or Electronic Health Records - Digitally stored healthcare record (or part of it)for the whole life of a patient, aiming to support the continuity of patient care (quality, access, efficiency) education and research.

Primarily used for Patient Care and Management of Care and Quality assessment

Healthcare professionals-Medical doctors, nurses and other staff Health managers & Health authorities, Epidemiologists etc

High tech act

2009 – Health Information Technology for Economic and Clinical Health (HITECH) Catalyzed HIT adoption and the broad adoption of EHRs. Provided incentives and support for care providers to adopt EHRs and allow the information to be used to improve patient outcomes

Call bell

A device used by a patient to signal his or her need for assistance from professional staff ???

Oncology informatics

Big Data Analytics and AI Can be used in oncology to determine difference between benign and malignant growths

Direct to consumer genetic testing

The FDA has given direct-to-consumer genetic testing (such as 23&Me) a cautious green light ---Tests are reproducible, accurate, and can identify genetic variants

Direct-to-consumer genetics also faces concerns regarding the ability of providers to make claims about their testing capability

While anyone can purchase a genetic testing kit, fewer people have the literacy to comprehend the implications

Consumer genetics

Access to electronic devices

Due to increased availability of electronic devices, development in electronic health access and information sharing has increased Patients have improved access to electronic devices and can have their data tracked to help inform clinical decisions

Health informatics in health care delivery

Genomics —Impact: direct-to-consumer genetic testing; addresses health concerns early; issue → companies that send this don’t understand when clients need this testing or when result interpretation is appropriate —FDA given cautious green light - tests are reproducible, accurate, identify genetic variants

Mobile health —Impact: allows pts to obtain information on their own by accessing portals, EHRs, social media, health related apps, etc —Convenient visible, reliable, secure, and accessible health management model beneficial as move into future —Wearable technology —Text message reminders —Requires guidelines for accessing, sharing, and monetizing data

Precision medicine —Impact: Provides specialized platforms for accessing, designing, and implementing unique tx plans —Goal is to use all healthcare info available to maximize possible benefit from tx a patient may see

Telehealth —Impact: Allows pts to engage w/ healthcare providers at home as family members and pt themselves track and monitor results —Synchronous or asynchronous —Supports pt engagement —Should reduce hospitalizations, improve QOL, and improve effectiveness of health interventions

Benefits pt and organization by decreasing cost for pt and facility Improves operational efficiency and prioritizes health access

AI and machine learning

Machine learning → technology that learns from data to enable a system function Taxonomy of techniques includes supervised learning, unsupervised learning, reinforcement learning, deep learning

Penalties for HIPPA (Criminal and/or civil?)

both