Integrating a Categorial Structure for Clinical Practice into EHRs.

A continuing global desire to be using clinical systems within a digital health ecosystem, able to facilitate data flows and information exchange as required to support person-centred, predictive, preventative, participatory and precision (5p) health and medical care can best be supported through the use of the standard categorial structure able to represent not only the clinical nursing practice domain but also other clinical disciplines by the generic labelling of some high-level categories. It is hypothesised that adoption of this generic clinical categorial structure within any electronic health/medical record within a well connected digital health ecosystem, supported by a cloud based openEHR platform, will enable the 5p support to be realized. This presentation provides the results of the latest update of this technical standard based on the 20+ year nursing practice categorial structure development process adopted to achieve this aim and a summary about linking this categorial structure to standard terminologies and to standard EHR/EMR system architectures.

Recommendations of the International Medical Informatics Association (IMIA) on Education in Biomedical and Health Informatics: Second Revision.

BACKGROUND: The purpose of educational recommendations is to assist in establishing courses and programs in a discipline, to further develop existing educational activities in the various nations, and to support international initiatives for collaboration and sharing of courseware. The International Medical Informatics Association (IMIA) has published two versions of its international recommendations in biomedical and health informatics (BMHI) education, initially in 2000 and revised in 2010. Given the recent changes to the science, technology, the needs of the healthcare systems, and the workforce of BMHI, a revision of the recommendations is necessary. OBJECTIVE: The aim of these updated recommendations is to support educators in developing BMHI curricula at different education levels, to identify essential skills and competencies for certification of healthcare professionals and those working in the field of BMHI, to provide a tool for evaluators of academic BMHI programs to compare and accredit the quality of delivered programs, and to motivate universities, organizations, and health authorities to recognize the need for establishing and further developing BMHI educational programs. METHOD: An IMIA taskforce, established in 2017, updated the recommendations. The taskforce included representatives from all IMIA regions, with several having been involved in the development of the previous version. Workshops were held at different IMIA conferences, and an international Delphi study was performed to collect expert input on new and revised competencies. RESULTS: Recommendations are provided for courses/course tracks in BMHI as part of educational programs in biomedical and health sciences, health information management, and informatics/computer science, as well as for dedicated programs in BMHI (leading to bachelor's, master's, or doctoral degree). The educational needs are described for the roles of BMHI user, BMHI generalist, and BMHI specialist across six domain areas - BMHI core principles; health sciences and services; computer, data and information sciences; social and behavioral sciences; management science; and BMHI specialization. Furthermore, recommendations are provided for dedicated educational programs in BMHI at the level of bachelor's, master's, and doctoral degrees. These are the mainstream academic programs in BMHI. In addition, recommendations for continuing education, certification, and accreditation procedures are provided. CONCLUSION: The IMIA recommendations reflect societal changes related to globalization, digitalization, and digital transformation in general and in healthcare specifically, and center on educational needs for the healthcare workforce, computer scientists, and decision makers to acquire BMHI knowledge and skills at various levels. To support education in BMHI, IMIA offers accreditation of quality BMHI education programs. It supports information exchange on programs and courses in BMHI through its Working Group on Health and Medical Informatics Education.

Nursing Informatics Integration into Mainstream Health Informatics.

Nursing Informatics emerged in Australia during the early 1980s and drove the Professional development and acceptance of Health Informatics. Milestones achieved include the development of a national journal, the establishment of the Health Informatics Society of Australia and the Australasian College of Health Informatics (now collectively the Australasian Institute of Digital Health), nursing participation in Health Informatics standards development activities, adoption of the HL7 messaging standard, the delivery of numerous workshops, an annual national health informatics conference since 1993, hosting international conferences, the development and delivery of Health Informatics post graduate programs and establishing a research centre where the first prototype for an archetype repository was developed. This became the openEHR Clinical Knowledge Manager. The most recent milestone was the establishment of a private company that became a Registered Training Organisation. Continuing challenges include workforce capacity building to address the poor understanding of the need for improved data and IT governance at every level, the need to comply with proven scientific and technical principles and a need to transform national and international traditional infrastructures no longer fit for purpose to enable adequately support for global sustainable digital health ecosystems. Desired personal and aggregate data supply chains must be taken seriously and be supported by the best available technologies. Our collective biggest challenge is to improve multidisciplinary and intersectoral collaboration, semantic interoperability and optimum digital support to maintain global public health.

Applying the Nursing Ecosystem Concept to Developing Strategic Innovation and Entrepreneurial Leadership for Valued Patient Care.

Research confirms entrepreneurial leadership encourages entrepreneurial behaviour and an entrepreneurial culture supports the development of 'entrepreneurial mindset'. Nurses implementing and optimizing information technology need to work with numerous stakeholders that collectively make up their ecosystem. Indeed, nurses with an entrepreneurial mindset increase their ability to sense opportunities and mobilize the resources and knowledge required to seek' informatics' opportunities to deliver patient centred care across the whole ecosystem.

Technology Informatics Guiding Education Reform - TIGER.

BACKGROUND: While health informatics recommendations on competencies and education serve as highly desirable corridors for designing curricula and courses, they cannot show how the content should be situated in a specific and local context. Therefore, global and local perspectives need to be reconciled in a common framework. OBJECTIVES: The primary aim of this study is therefore to empirically define and validate a framework of globally accepted core competency areas in health informatics and to enrich this framework with exemplar information derived from local educational settings. METHODS: To this end, (i) a survey was deployed and yielded insights from 43 nursing experts from 21 countries worldwide to measure the relevance of the core competency areas, (ii) a workshop at the International Nursing Informatics Conference (NI2016) held in June 2016 to provide information about the validation and clustering of these areas and (iii) exemplar case studies were compiled to match these findings with the practice. The survey was designed based on a comprehensive compilation of competencies from the international literature in medical and health informatics. RESULTS: The resulting recommendation framework consists of 24 core competency areas in health informatics defined for five major nursing roles. These areas were clustered in the domains "data, information, knowledge", "information exchange and information sharing", "ethical and legal issues", "systems life cycle management", "management" and "biostatistics and medical technology", all of which showed high reliability values. The core competency areas were ranked by relevance and validated by a different group of experts. Exemplar case studies from Brazil, Germany, New Zealand, Taiwan/China, United Kingdom (Scotland) and the United States of America expanded on the competencies described in the core competency areas. CONCLUSIONS: This international recommendation framework for competencies in health informatics directed at nurses provides a grid of knowledge for teachers and learner alike that is instantiated with knowledge about informatics competencies, professional roles, priorities and practical, local experience. It also provides a methodology for developing frameworks for other professions/disciplines. Finally, this framework lays the foundation of cross-country learning in health informatics education for nurses and other health professionals.

Operationalising the National Nursing Informatics Position Statement.

The need for nurses and midwives to drive the digital transformation of care has been recognised as an important factor in future health care innovation. An Australian Chief Nursing Informatics Officers collaboration is leading the way. They were supported by the three professional organisations who collaboratively developed a National Nursing Informatics position statement. This paper describes the development and use of the elements described in the statement in operationalising Nursing informatics within individual healthcare organisations and for driving national digital transformation initiatives.

Connecting PHRs and EHRs for a Sustainable National Health System.

An EHR for integrated care (IEHR) is defined by the International Organization for Standardization (ISO) [1]: "…a repository of information regarding the health status of a subject of care, in computer processable form, stored and transmitted securely, and accessible by multiple authorised users, having a standardized or commonly agreed logical information model that is independent of EHR systems and whose primary purpose is the support of continuing, efficient and quality integrated health care. It contains information which is retrospective, concurrent and prospective." We need to differentiate between EMR/EHR and the lifelong PHR in terms of type of data storage, sharing and use [2-3].

Embedding Nursing Informatics Education into an Australian Undergraduate Nursing Degree.

Alongside the rapid rise in the adoption of electronic health records and the use of technology to support nursing processes, there is a requirement for nursing students, new graduate nurses, and nursing educators to embrace nursing informatics. Whilst nursing informatics has been taught at post graduate levels for many years, the integration of it into undergraduate studies for entry level nurses has been slow. This is made more complex by the lack of explicit nursing informatics competencies in many countries. Australia has now mandated the inclusion of nursing informatics into all undergraduate nursing curricula but there continues to be an absence of a relevant set of agreed nursing competencies. There is a resulting lack of consistency in nursing curricula content nationally. This paper describes the process used by one Australian university to integrate nursing informatics throughout the undergraduate nursing degree curriculum to ensure entry level nurses have a basic level of skills in the use of informatics.

Learning, Training and Teaching of Health Informatics and its Evidence for Informaticians and Clinical Practice.

A globally agreed well structured framework representing the health informatics discipline's body of knowledge is yet to emerge. Considerable progress has been made towards describing this over the fifty or so years of the discipline's evolution. This contribution explains the need for such a structured body of knowledge from an educational and workforce capacity building perspective. Some examples of how education and training has been provided to date by a few key stakeholders/leaders are given and critical reviews of guideline and competency developments and their applications are presented. This is followed by an explanation of the need for linking health informatics research with education, learning and training strategies and desired future directions to overcome the identified health workforce knowledge and skills gaps are explored. Given the increasingly important role of health IT in health care, and the significant investment being made into Health IT systems and infrastructure, it is illogical not to seriously invest in health workforce capacity building.

An Information Paradigm Shift is Required to Realize EHR Benefits.

The use of EHRs and the benefits from them are significant for healthcare and health informatics. Data form the basis for any EHR and its potential to realize these benefits. This paper considers the housing of information and knowledge management in an EHR system. Are we experiencing a revolution in healthcare? Findings from an investigation of alternative approaches, followed by an evaluation of the importance of the adoption of a standard information model relative to benefit realization, is presented. We conclude that an EHR in any environment is not limited to sharing or information exchange. A paradigm shift in thinking, based on the requirement for standardized concept representation, is required. This is an essential prerequisite for a new vision of healthcare supported by digital technologies.

National healthcare systems and the need for health information governance.

This chapter gives an overview of health data, information and knowledge governance needs and associated generic principles so that information systems are able to automate such data collections from point-of-care operational systems. Also covered are health information systems' dimensions and known barriers to the delivery of quality health services, including environmental, technology and governance influences of any population's health status within the context of national health systems. This is where health information managers and health informaticians need to resolve the many challenges associated with eHealth implementations where data are assets, efficient information flow is essential, the ability to acquire new knowledge desirable, and where the use of data and information needs to be viewed from a governance perspective to ensure reliable and quality information is obtained to enhance decision making.

Impact of data governance on a nation's healthcare system building blocks.

This chapter gives an overview of a nation's healthcare system, particularly for those who are familiar with IT but not healthcare or for those working in one area of healthcare who may not be familiar with the system and data requirements across the care continuum. The structure of this chapter uses the World Health Organisation's (WHO) Health systems framework with a focus on the need for data and information governance to achieve a sustainable health system delivering improved health for all, responsively and equitably meeting genuine demands for health services, with social and financial risk protection and overall improved efficiency. It is argued that there is a need to gather the right data and to process these data in a manner that provides good information in order to more fully understand how the health system is working and where and when it isn't working well. This needs to be achieved in the most cost effective manner that doesn't detract from the allocation of resources to healthcare or the clinical workflow required to achieve quality healthcare.

Health data and data governance.

Health is a knowledge industry, based on data collected to support care, service planning, financing and knowledge advancement. Increasingly there is a need to collect, retrieve and use health record information in an electronic format to provide greater flexibility, as this enables retrieval and display of data in multiple locations and formats irrespective of where the data were collected. Electronically maintained records require greater structure and consistency to achieve this. The use of data held in records generated in real time in clinical systems also has the potential to reduce the time it takes to gain knowledge, as there is less need to collect research specific information, this is only possible if data governance principles are applied. Connected devices and information systems are now generating huge amounts of data, as never before seen. An ability to analyse and mine very large amounts of data, "Big Data", provides policy and decision makers with new insights into varied aspects of work and information flow and operational business patterns and trends, and drives greater efficiencies, and safer and more effective health care. This enables decision makers to apply rules and guidance that have been developed based upon knowledge from many individual patient records through recognition of triggers based upon that knowledge. In clinical decision support systems information about the individual is compared to rules based upon knowledge gained from accumulated information of many to provide guidance at appropriate times in the clinical process. To achieve this the data in the individual system, and the knowledge rules must be represented in a compatible and consistent manner. This chapter describes data attributes; explains the difference between data and information; outlines the requirements for quality data; shows the relevance of health data standards; and describes how data governance impacts representation of content in systems and the use of that information.

Our health language and data collections.

All communication within the health industry is dependent upon the use of our health language consisting of a very extensive and complex vocabulary. Converting this language into computable formats is necessary in a digital environment with a strong reliance on data, information and knowledge sharing. This chapter describes our health language, what terminologies and ontologies are, their use and relationships with natural language, indexing, data standards, data collections and the need for data governance.

Health information systems.

Health information provides the foundation for all decision making in healthcare whether clinical at the bed side, or at a national government level. This information is generally collected as part of systems which support administrative or clinical workflow and practice. This chapter describes the many and varied features of systems such as electronic health records (EHRs), how they fit with health information systems and how they collectively manage information flow. Systems engineering methods and tools are described together with their use to suit the health industry. This focuses on the need for suitable system architectures and semantic interoperability. These concepts and their relevance to the health industry are explained. The relationship and requirements for appropriate data governance in these systems is also considered.

Health workforce competencies needed for a digital world.

The health workforce constitutes a very significant health system building block. As such it needs to have the capacity to influence how health data are captured, processed and used at all levels of decision making. This requires a national strategy that ensures all new health professional graduates are adequately prepared and that the existing workforce is developed to make the best possible use of all available digital technologies. This chapter provides an argument for why and how the health workforce should be contributing to health information governance, followed by an historical overview of various initiatives undertaken, the results achieved and issues identified during these processes. It concludes with an exploration of strategies that may be adopted to bring about change and achieve improvements.

Developing a common reference model for the health informatics discipline.

This study reports on an examination of the IT industry's Skills Framework for the Information Age (SFIA) to determine if such a process and/or the use of this tool is suitable to be applied to the health informatics discipline. During this process, four sets of known and agreed-upon Health Informatics skill and knowledge domain statements and competencies were mapped to SFIA. The results showed that all high level SFIA skills apply to the health informatics discipline but that these need to be contextualized to suit the health industry and additional health industry specific skills and knowledge domains need to be included to truly reflect the necessary health informatics skill set from which competency statements can be developed. Adoption of an accord similar to the Seoul Accord process could be very beneficial in promoting a global understanding of the health informatics discipline.

Health informatics competencies - underpinning e-health.

There is a widespread consensus that we have an urgent need to improve our workforce capacity in all aspects associated with the skills and knowledge required for successful e-health and health informatics developments, associated change management and systems implementation strategies. Such activities aim to support various health reform policy initiatives. This paper considers the work being undertaken by many researchers around the globe to define the range of skills and knowledge requirements to suit this purpose. A number of requirements and areas of specialisation are detailed. This is followed by descriptions for competencies in general and more specifically descriptions of a set of high level agreed Health Informatics competencies. Collectively these competencies provide a suitable framework useful for the formal recognition of Health Informatics, including e-health, as a nationally recognised study discipline. Nationally agreed competencies for this discipline enables all education and training efforts to be consistently implemented and to fit with the Australian Qualifications Framework covering both the Vocational Education and Training (VET) and Higher Education sectors.

Foundations for a nursing services reference model.

The Nursing Services Reference Model (NSRM) is presented as a theoretical position and discussion paper. The aims are to describe the components of the NSRM concept, to explain why such a model needs to be developed and to explore methodological issues in the development of a NSRM. The concept is important to address as it may illuminate a most pressing problem faced by the Australian health care industry where the content and activity of nursing practice is not embedded as computer processable data in health information system structures. Digital documentation of nursing content and activity is urgently needed to enable reliable electronic processing of nursing services. However, it is necessary, prior to this, to develop a reference model that describes the range of nursing services in an unambiguous manner.