CHIA F Flashcards
Kahneman and Tversky disrupted mainstream economics by demonstrating that decisions are not always optimal. Their ‘prospect theory’ showed that
humans’ willingness to take risks is context-dependent – i.e., it is influenced by the way choices are framed (Samson, 2014). Essentially, we dislike losses more than we like an equivalent gain. The pain of giving something up is greater than the pleasure of receiving it.
According to dual-system theory of behavuoural economics
System 1
• Comprises thinking processes that are intuitive, instinctive, and experience-based.
• Associated with heuristics (cognitive shortcuts), biases (systematic errors), and aversion to change.
System 2
• Comprises thinking processes that are reflective, controlled, deliberative, and analytical.
• Associated with agency, choice, and concentration.
‘Market failure’ refers to
a situation where the market does not deliver an efficient outcome, which generally occurs in cases where private incentives are misaligned with the broader interests of society as a whole
Market power is
is exercised when one or more parties can ‘coerce’ others. Examples include:
• Large and powerful suppliers (monopolists or oligopolists) who can extract higher prices from their customers than they could in more competitive markets.
• Large and powerful customers (monopsonists and oligopolists) who can extract lower prices from their suppliers.
The effects of market power may feed into cost-benefit or effectiveness analysis is in the valuation of costs or benefits. However, they may also require regulatory intervention.
‘Public goods’ in health economics are:
are goods or services that are ‘non-rivalrous’ (one person consuming the good does not prevent others from also consuming it) and/or ‘non-excludable’ (it is impractical to exclude people from benefiting from the good, once it is made available). A classic example is clean air. Pragmatically, one person breathing clean air does not stop others from doing so, and once clean air is available, it is difficult to prevent anyone from breathing it. Consumers fail to pay for a public good because they cannot be excluded from the benefits are known as ‘free-riders’.
Health information is often a public good. Population health services such as clean air, food safety and vector control may also be public goods. One person consuming them does not prevent others from doing the same, and once they are provided, it may be difficult to stop anyone from realising the benefits.
What are exernalities in health economics
Externalities occur when the consumption of certain goods and services deliver benefits to or impose costs upon unrelated third parties. These are positive and negative externalities, respectively. For example:
• Vaccination has the benefit of protecting its direct consumer against illness but may also protect the spread of disease to others, enabling them to benefit. This is a positive externality.
• Sugar prices typically do not account for the public health costs of excess societal sugar consumption. This constitutes a negative externality.
What are indirect network exernalities in health economics
Network effects are one specific form of externality that health informaticians are likely to encounter.
Indirect network externalities concern complementary goods and services. For example, the value of a computer peripheral such as external speakers increases with the range of computers they can operate with. On the other hand, cybersecurity threats are also complementary services. Cybersecurity threats have been rising rapidly in healthcare in recent years, spurred on in part by greater health IT usage. This is an example of a negative, indirect network externality.
Research covering capability requirements for digital transformation across 31 OECD countries and their partner economies suggests
• Despite automation, task-based (non-cognitive, learned on the job) skills remain as important as cognitive (learned through education) skills.
• Digitally-intensive industries reward workers with relatively higher levels of self-organisation, advanced numeracy skills, and communication and socioemotional skills.
• Bundles of synergistic skills are significant in digitally-intensive industries.
The World Economic Forum (WEF) identifies eight specific digital skills domains in which proficiency is likely to be required for people to feel “competent, comfortable, confident and safe in their daily navigation of a digitalised work and life environment
• Digital identity (digital citizen, digital co-creator, digital entrepreneur).
• Digital rights (freedom of speech, intellectual property rights, privacy).
• Digital literacy (computational thinking, content creation, critical thinking).
• Digital competencies (online collaboration, online communication, digital footprints).
• Digital emotional intelligence (social and emotional awareness, emotional regulation, empathy).
• Digital security (password protection, internet security, mobile security).
• Digital safety (behavioural risks, content risks, contact risks).
• Digital use (screen time, digital health, community participation).
Irrespective of the methodology used, education and training needs analysis typically involve four stages –
organisational analysis, operational analysis, person analysis, and training requirements analysis. Each of the first three stages aims to identify needs and ensure that the organisation’s needs, operational requirements, and people align. The fourth considers whether education and training are the best options, and if so, consolidates, and quality assures the requirements
Training needs analysis - Organisational analysis
Analysis of the organisational dimension of training needs aims to clearly articulate what the organisation requires of its people, irrespective of the specific roles they individually play.
Organisational analysis of training needs requires consideration of current performance and intentions (as signalled through strategic planning and other foresight processes).
Techniques for undertaking such organisational analyses include desk research (e.g., the perusal of plans, policies, strategies, performance reports, complaints, etc.), comparative research (e.g., literature searches, competency benchmarking, etc.), staff, consumer, and other stakeholder surveys, interviews, and focus groups. Dialogue, rather than passive data collection, is vital.
Training needs analysis - Operational analysis
Essentially, this analysis examines what the organisation, through its people, needs to do to achieve its strategic objectives.
Operational analysis involves examining the organisation’s activities and how they are performed. In the context of evolution towards digital health, this primarily means examining changes expected to what the organisation does (at an operational level) and how it will do it. However, in terms of current practice, it also means examining current performance, identifying existing strengths (for retention and consolidation) and weaknesses (for improvement), and identifying whether education and training gaps are associated with any of these.
Techniques for undertaking operational analysis include desk research (e.g., the perusal of operational documentation), comparative research (e.g., competency benchmarking), staff, consumer, and other stakeholder surveys, interviews, and focus groups.
Training needs analysis - Person analysis
Knowing the competencies and proficiency levels required enables the assessment of the people involved against these standards. Essentially, this means ascertaining which individuals need education and training – which people perform which roles and undertake which activities? What is their assessed proficiency in terms of the competencies required? What are the gaps?
Techniques for person analysis include desk research (e.g., the perusal of performance assessments and education and training records), direct observation of staff in the role, work samples, and staff interviews.
Training needs analysis
This training requirements analysis step involves working out the optimal strategies for ending up with the right competencies in the right places at the right time. Once these strategies are determined, the aggregate education and training needs will be visible, and prioritisation can occur. At this point, it is worthwhile:
• Conducting a quality assurance exercise to ensure that the competencies required are well enough specified to enable educators and trainers to determine how they can best be delivered.
• Undertaking ‘due diligence’ – i.e., validating that the costs of the education and training proposed are likely to generate sufficient returns to justify them.
barriers to digital health innovation and education (3)
• Lack of content and lack of demand where it does exist.
“Some concern from universities, colleges and accreditation providers about the addition of digital health content in curricula due to ‘curriculum crowding’” and “limited demand for digital health-focused subjects in universities, possibly due to a perception that these are only applicable to health informaticians”.
• Resource constraints.
“Thin margins and, in many areas of the health sector, relatively small business scale (such as small general practices) that impose limitations on investment capacity” and “difficulty accessing training … versions of digital health used in state and territory health systems to provide students with ‘hands-on’ experience”.
• Professional resistance.
“Resistance to innovations that blur existing scope of practice boundaries, or which do not align with [existing] funding models”.
Data design - Data objects, attribtes and relationships
data objects (data entities or concepts with common properties which are stored and operated upon during the running of a software program, e.g., actors (such as persons, equipment, etc.), roles (such as citizens, patients, health service providers, etc.) and events (such as consultations, admissions, transfers of care, etc.)
and their attributes (descriptions of the objects’ properties, e.g., first, last, and other names, date of birth, gender, etc. in the case of persons)
and their relationships (descriptions of how different data objects may be associated (e.g., a person may be a citizen, a patient and/or a health service provider) or data objects may be related to their attributes (e.g., a person may have multiple other names but can have only one date of birth)
This typically includes documentation in the form of an information model.
In data design, the data objects, attributes, and relationships articulated during the analysis phase of the system life cycle are reconceptualised as: (4)
data types (e.g., alphanumeric – string, text, or formatted text; date/time – date, time, or timestamp; time-series – date/time range, repeat interval, timing/quantity)
data structures (specific ways of organising data in computer programs so that it can be used efficiently and effectively – more on this shortly)
the integrity rules required to ensure the data is what it purports to be, and
the operations that can be applied to the data structures.
characteristics of information that are associated with fitness for purpose include (9)
Provendance
The instititional envrironmenbt
Relevance
Completeness and validity
Timeliness
Accuracy and precision
Coherence
Interpretability
Accessibility
Analysis of data needs:
consideration of context (regulation, community expectations, applicable data principles, policies, and strategies) and capability (possession of or access to the competencies and resources required to design, develop, manage, and maintain data throughout its life cycle) as well as data functionality (how can it be appropriately used?).
Analysis of data usage:
concerns how, where, when, and in what forms various users can access the data and the access rights they have – e.g., to modify or delete.
New data design and development processes begin when existing data does not meet the identified needs. In brief (4)
• Data items are specified. They are named and defined in meaningful ways, and their attributes are articulated and documented as metadata (information about the data that helps users understand and accurately interpret it). This should consider relevant standards that facilitate safe and effective use, reuse, and interoperability.
• Data capture and quality assurance instruments and processes are developed or otherwise actioned (e.g., some data may be purchased), and data processing (e.g., cleansing, transformation, manipulation, etc.), storage and retrieval mechanisms are developed, tested, and actioned.
• Data presentation formats and delivery channels are developed, tested, and actioned.
• Data usage and utility (value derived) are then monitored and assessed throughout the data lifecycle, with modifications as required.
Essential steps to appraise the structure and design of health information
- Confirming and validating the different use contexts and ensuring these are documented appropriately.
- Identifying the data, information, knowledge, and wisdom required to inform these uses and the characteristics that would make these fit for purpose
- Assessing design characteristics – is the metadata readily available? Is it well constructed? Does it comply with regulatory requirements? Does it conform to relevant standards? Do the data types permit and facilitate the processing required (e.g., can arithmetic operations be performed if needed)? Do the data structures allow and facilitate the processing necessary (e.g., do they enable ‘fuzzy logic’ to be applied)?
- Assessing usage – does the information satisfy the needs of all its existing and potential users.
Data attributes can be
• Simple – attributes that cannot be split into other attributes (e.g., first name).
• Composite –groupings of other attributes (e.g., name comprising first, last, and other names).
• Derived – attributes that are calculated or determined from other attributes, such as age calculated from date of birth.
• Single-value – attributes only captured once (e.g., first name, with alternatives being aliases).
• Multi-Value – attributes that can be captured more than once for an entity (e.g., multiple mobile phone numbers).
Some principles to guide the nature and extent of attribute elaboration include: (5)
• Compliance with relevant regulations and policies, including privacy.
• Restricting the attributes to those reasonably necessary for, or directly related to, the organisation’s purpose and functions.
• Recognition that some attributes might involve sensitive information.
• Representing attributes in meaningful ways (relevant, complete, valid, interpretable) that can be captured with high quality (accurate, precise, coherent).
• Documenting metadata appropriately (such that others can unambiguously and sufficiently understand the data).