Health Informatics Ethics: Artificial Intelligence, Machine Learning, and Ethics with respect to Healthcare informatics. Standards and Public Policy.

1. Health
Informatics
BCA-2020: Semester-V

2. Module 5:
Chapter 2
INFORMATION PRIVACY
AND SECURITY
HEALTH INFORMATICS
ETHICS
BIOINFORMATICS

3. Module
Content
 Information Privacy and Security:TheValue and Importance of
Health Information Privacy, security of health data, potential
technical approaches to health data privacy and security.
 Health Informatics Ethics: Artificial Intelligence, Machine
Learning, and Ethics with respect to Healthcare informatics,
Ethics, Standards and Public Policy.
 Bioinformatics: Bioinformatics, Healthcare Informatics and
Analytics for Improved Healthcare System, Intelligent Monitoring
and Control for Improved Healthcare System.

4. LearningObjectives
 Describe the medical and computing background
to health informatics ethics
 Describe the complexities in the relationship
between ethics, law, culture and society
 Discuss the application of health informatics
ethics to research into pertinent areas of health
informatics
 Discuss appropriate health informatics behavior
by medical students

5. Artificial
Intelligence in
Health
Informatics
 In this era of Big Data, supercomputing, advanced technology,
extensive research, and seemingly non-ending pandemics like
COVID-19, Health Informatics (HI) has the potential to minimize
the data gap in public health between doctors, scientists,
governments, and people.
 But the question is, “Are we making good use of these large
untailored piles of data in the right way? Or, are the traditional
computing tools and research procedures sufficient to analyze
these data accurately?”
 These questions have only one answer:Artificial Intelligence (AI),
an outstanding combination of computing power with human
cognition capable of revolutionizing the healthcare industry

6. Artificial
intelligence
(AI) in
Medicine
 Artificial intelligence (AI) generally applies to computational
technologies that emulate mechanisms assisted by human
intelligence, such as thought, deep learning, adaptation,
engagement, and sensory understanding.
 Some devices can execute a role that typically involves human
interpretation and decision-making.These techniques have an
interdisciplinary approach and can be applied to different fields, such
as medicine and health.
 Artificial intelligence (AI) in Medicine arose in the 1970’s. First AI
systems were essentially knowledge-based decision support systems
and first machine learning methods were used for inferring
classification rules from labelled datasets.
 These first systems had good performance. However, they were never
used routinely on real patients.
 One reason was that these systems were standalone systems, not
connected with patient electronic health records (EHRs).
 Another reason was that, due to the subjectivity of the expertise
expressed in the knowledge bases of these expert systems, the systems
developed here were not accepted there, and most of them turned out
to be more useful for teaching than for clinical practice.

7. Advantages of
Artificial
Intelligence in
Health
Informatics
(AIHI)
 1. Disease Identification and Prevention of an Outbreak
 The first and most crucial step in drug discovery (DD) is disease
identification. From the healthcare data, AIHI can predict the
symptoms, complications, contamination rate, severity, recovery
rate, how long a disease affects, whether the condition is new or
reported, and the geographical area of the population it is
affecting.
 Monitoring, managing, and analyzing these data is easy for
humans in a small quantity; but, for a wide range such as in a
global range,AIHI is a better fit.

8. Advantages of
Artificial
Intelligence in
Health
Informatics
(AIHI)
 2. Precision Medicine and Drug repurposing
 Drug repurposing/repositioning is the widely used trend in modern
DD due to its economic and re-useful impact. Precision medicine that
aims to personalize treatment for every individual is the future face of
global healthcare infrastructure and biomedical sciences.
 Both fields need close monitoring and analysis of every patient’s
medical record and studies.AI-baked smartphones, smartwatches,
smart sensors, digital stethoscopes, glucometers, portable x-ray
machines, and cloud storage have made daily life effortless.
 AIHI has enabled an app or software to monitor sleep, blood-sugar
level, X-ray, pulse, and catering of remote care with telemedicine. AI
can analyze the data obtained from these popular devices and filter
the necessary information with condensed and accurate insight in
real-time.
 By seeing the symptoms of the disease, the future of AIHI can give
precise options for drug repurposing and better treatment at the
individual level.

9. Advantages of
Artificial
Intelligence in
Health
Informatics
(AIHI)
 3. Better Monitoring in ClinicalTrial
 Clinical trial (CT) is a very crucial and meticulous process in DD. It
decides the fate of a new drug whether the drug will proceed to
market or go back to the lab. CT professionals face enormous
hurdles in their job.
 Appropriate volunteer selection in the early phase of the trial,
recruitment of suitable patients to avoid later dropout rate, close
observation and regulation of drug effect in patients and market in
later stages.
 Modern CT is complex with so many regulations, protocols, and
high costs with high failure rates, as the current approval of new
drugs has fallen to 5%. From the individual EHRs, AIHI will help CT
professionals to select volunteers and patients who can meet the
specific requirements of a particular trial.
 The new advancements inAIHI will automate patient observation
and regulation of drug effects concerning cost and effort.

10. Advantages of
Artificial
Intelligence in
Health
Informatics
(AIHI)
 4. Assistantship to Healthcare Professionals and Researchers
 AI automates the process, whereas HI keeps everyone related to
the specific study for the recent changes in the procedure,
protocols, demonstrations, and findings.
 AIs can automatically append the instant changes to the HI
database and alert personnel related to the study in a classified
and summarized manner.
 This automation will be time and labor-saving for healthcare
practitioners, researchers, nurses, suppliers, related patients, and
authorities

11. Advantages of
Artificial
Intelligence in
Health
Informatics
(AIHI)
 5. Awareness andTransparency
 The general public never had this much access and awareness
about ongoing health issues, treatment, procedures, drug price,
distinguishing counterfeit drugs, and necessary safety measures.
 Thanks to the IT and CS sectors that made the availability of
information possible at everyone’s fingertip.
 AIHI will help keep track of the pharma supply chain and abolish
the deficiency of medicines, injections, and other medical
equipment (i.e., the recent oxygen crisis in India).
 AIHI has the potency to stop the black drug market and the illegal
manufacturing of drugs.To solve criminal cases, fingerprint data
and genome data will help a lot in less time.

12. Artificial
Intelligence,
Machine
Learning, and
Ethics
 We want health informatics to save and improve lives, to reduce
suffering, to help to achieve the larger goals of the healthcare
professions. If an intelligent machine can help do that, why is
there a problem or any controversy?
 Key and core ethical issues in the development and use of
machine learning programs:
 Quality and Standards Are Ethical Issues
 Prevent and Eliminate Bias
 Use Machine Learning Software for Good and not Evil
 Insist on and Provide Robust Education and Evaluation

13. Artificial
Intelligence,
Machine
Learning, and
Ethics
 Quality and Standards Are Ethical Issues
 Good software conforms to certain standards for quality which
can be assessed in terms of trustworthiness and reproducibility.
 The mark or measure of good software will include accuracy of
documentation and transparency about the source of any code
components.
 This facilitates understanding, corrections, and improvements. It
follows that if one is developing or modifying machine learning
software, the automated learning process itself must also be
monitored and documented.
 Relatedly, careful software version control is an essential part of
high-quality programming.
 The values of transparency, veracity, and accountability reinforce
the connection among quality, standards, and ethics .

14. Artificial
Intelligence,
Machine
Learning, and
Ethics
 Prevent and Eliminate Bias
 A sure way to erode confidence in artificial intelligence is to identify
ways in which a deep learning algorithm embeds racial, ethnic,
gender, or other biases which shape or corrupt its results.
 The very nature of machine learning algorithms makes plain that one
might unintentionally develop a biased system or accept biased
results.
 If datasets used for training machine learning algorithms include or
entail bias or foster biased interpretations, then careful scrutiny of
such sets is a difficult and labor-intensive approach to take.
 Another is careful screening of output to identify and filter bias, and
perhaps to identify ways to modify the algorithm to suppress it.
 It is now even possible to incorporate anti-bias features into the code
itself .This is an extraordinarily promising approach, and something
like it might very well be the best way to ensure that public health
surveillance and prediction, disease diagnosis and treatment, and
health policy are not infected or corrupted by illicit bias.

15. Artificial
Intelligence,
Machine
Learning, and
Ethics
 Use Machine Learning Software for Good and not Evil
 Current relativist trends and fashions notwithstanding, some
actions are good and others are bad and there is little credible
dispute about their moral status.
 Reducing suffering, eliminating disparities, and improving health
are good; depriving people of rights, using people for political or
economic purposes without permission given voluntarily, and
harming people for profit are bad.
 Is there a chance that a clinical decision support system will
improve diagnosis, treatment, and prognosis and simultaneously
erode confidence in the clinician-patient relationship?
 May a nurse with a good computer system undertake duties
traditionally reserved for physicians?Will the use of intelligent
machines in health care cause the erosion of healthcare
practitioner clinical skills?Addressing these and other challenges
successfully will require sustained research, education, and
debate.

16. Artificial
Intelligence,
Machine
Learning, and
Ethics
 Insist on and Provide Robust Education and Evaluation
 No technology will ever fulfill its promise or be used in an ethically
optimized manner unless we insist on and provide robust education
and evaluation.
 Scientists and clinicians must not only be taught empirical methods
and clinical skills, but also the appropriate uses of these methods and
skills.
 Given our concern for appropriate uses of intelligent machines in
healthcare, it follows we must also identify appropriate users, and a
core criterion for demonstrating such appropriateness will be a user's
fitness to use the tool. Making and acting on this assessment has long
been recognized as a key ethical challenge when computers are used
in healthcare.
 We are, moreover, in urgent need of a comprehensive ethical
curriculum in and for health informatics.We must also not lose sight
of the importance of system (algorithm, device) evaluation in the
context of its actual use.

17. Ethics,
Standards, and
Public Policy
 The evolution of standards in health care has improved quality,
increased safety, and conserved resources.This is especially true
for health informatics.
 Standards are also an important component of public policy and
one of the ways by which applied ethics informs and shapes public
governance.
 From the foundations of software engineering to the design of
electronic health records to embedded privacy protections to
evaluation and interoperability, ethical principles and standards
serve as both guiderails and signposts.
 Is there a problem with biased algorithms?
 Adopt standards for better software and testing.
 Do members of communities distrust those who collect and analyze
personal information?
 Follow standards for trust enhancement.
 Confused about whether to adopt a new technology?
 Then turn to ethical standards for harm reduction and rights
protection.

18. ThankYou!