Ebook: Health Information Governance in a Digital Environment

As long standing participants in the development of Health Informatics standards nationally and internationally we have become increasingly aware of the many challenges being faced by those who design, develop and implement information systems in the health industry. We are witnessing the impact of major social and industrial changes resulting from a rapid expansion in the design, development and use of information technologies. When applied to the health industry, and especially when applied to the processing of clinical data, the need for understanding health data and information governance is clear.

Health data and information are primary assets of the health industry. Such assets need to be used optimally to ensure we are able to meet the health and health care needs of the population in a timely, responsive and sustainable manner. That requires the ability to appropriately collect, consistently define, accurately aggregate, link, relate to knowledge and machine process health data accurately. Health language is extensive and complex, and includes a lot of jargon, and abbreviations. Computers require consistency for accurate, comparable data and information processing, making the management of health data and information in this new and continually expanding digital environment a major challenge.

A high degree of semantic interoperability between systems in the health industry is essential. To deliver the benefits claimed from the use of information and communication technologies in healthcare, computer systems need to be able to communicate and apply the meaning of data – not just the codes. This is about issues ranging from data at the bed side through to ‘big data’, mass collaboration, data sharing, and imminent changes to our understanding and use of intellectual property. Achieving this requires the adoption of ‘disruptive technology’, as health workers need to be able to accommodate many innovations.

As health informatics/information management educators we engage in a fairly constant process of health workforce skill and knowledge gap analysis. Once gaps are identified we explore how best to meet these educational needs. This process is influenced by our vision of a sustainable national (if not global) health system and a concurrent evaluation of educational trends. During this process we have been privileged to engage with many experts around the world, and share many experiences associated with health information management; and system design, development, implementation and use. We have had many passionate debates, witnessed failures and successes and above all we have been able to share our vision.

We have also been able to keep up to date with technological advances and learned about the contributions made by many related disciplines. We continue to benefit from the ongoing learning opportunities that come our way as a result of serious voluntary engagement in the national and international health informatics standards development processes. The Health Informatics discipline is huge and complex, and its development relies heavily on multidisciplinary teamwork.

Why is it that other industries appear to be much further advanced in the use of digital technologies than the health industry? The answer may be that we simply haven't been able to optimally manage our data and information assets or the changes necessary to enhance the technology. This book is another step towards assisting you to collaboratively meet this challenge.

This text is not all inclusive or exhaustive. It builds on a previous book, Health Informatics: an overview published in 2010. This new book provides an overview of national health care systems, described using the World Health Organisation's health systems framework for those new to the health industry. It has a focus on health data and information governance but also includes detailed information about such topics as data collection, data definitions, data aggregation, data linkages, digital knowledge representation and computer processing relationships.

This publication is divided into three sections: Setting the Scene, Digital Knowledge Management and Using Health Data. With contributions from distinguished authors, this book is a valuable resource for policy and decision makers, healthcare professionals, students of health information management and health informatics, and ICT professionals wishing to work in the health industry.

Acknowledgements

We wish to acknowledge the contributions made by our very extensive network of people who share our passion.

Evelyn J.S. Hovenga

Heather Grain

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.

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 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.

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.

Protecting and preserving data stored in electronic form is important, and ensuring that data is available to the correct access level requires consideration of the characteristics of the data and the purpose to which the data will be used. Important questions therefore are raised about what is the right data and who has the right access level. This is the substance of data governance. This paper will discuss the various aspects of data governance frameworks as it pertains to health care systems. The paper will also explore the changes that confront organisations and individuals as they embrace the requirements of data governance.

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.

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.

This chapter identifies the skills, professional challenges and changes needed for health and IT workforce development, to support a team based integrated approach to the development and implementation of successful, cost effective, safe systems in healthcare. This requires an attitude which embraces change and breaks down existing hierarchical structures and responsibilities by recognising the need for different and extended knowledge and skills within each professional area and within any organisational workforce composition.

It is no small task to manage the protection of healthcare data and healthcare information systems. In an environment that is demanding adaptation to change for all information collection, storage and retrieval systems, including those for of e-health and information systems, it is imperative that good information security governance is in place. This includes understanding and meeting legislative and regulatory requirements. This chapter provides three models to educate and guide organisations in this complex area, and to simplify the process of information security governance and ensure appropriate and effective measures are put in place. The approach is risk based, adapted and contextualized for healthcare. In addition, specific considerations of the impact of cloud services, secondary use of data, big data and mobile health are discussed.

Information management can be a daunting process for clinicians, health care providers and policy makers within the health care industry. This chapter discusses the importance of information classification and information architecture in the information economy and specific challenges faced within the health care industry. The healthcare sector has industry specific requirements for information management, standards and specifications for information presentation. Classification of information based on information criticality and the value in the health care industry is discussed in this paper. Presentation of information with reference to eHealth standards and specifications for healthcare information systems and their key requirements are also discussed, as are information architecture for eHealth implementation in Australia. This chapter also touches on information management and clinical governance since the importance of information governance is discussed by various researchers and how this is becoming of value to healthcare information management.

This chapter describes the need for Detailed Clinical Models for contemporary Electronic Health Systems, data exchange and data reuse. It starts with an explanation of the components related to Detailed Clinical Models with a brief summary of knowledge representation, including terminologies representing clinic relevant “things” in the real world, and information models that abstract these in order to let computers process data about these things. Next, Detailed Clinical Models are defined and their purpose is described. It builds on existing developments around the world and accumulates in current work to create a technical specification at the level of the International Standards Organization. The core components of properly expressed Detailed Clinical Models are illustrated, including clinical knowledge and context, data element specification, code bindings to terminologies and meta-information about authors, versioning among others. Detailed Clinical Models to date are heavily based on user requirements and specify the conceptual and logical levels of modelling. It is not precise enough for specific implementations, which requires an additional step. However, this allows Detailed Clinical Models to serve as specifications for many different kinds of implementations. Examples of Detailed Clinical Models are presented both in text and in Unified Modelling Language. Detailed Clinical Models can be positioned in health information architectures, where they serve at the most detailed granular level. The chapter ends with examples of projects that create and deploy Detailed Clinical Models. All have in common that they can often reuse materials from earlier projects, and that strict governance of these models is essential to use them safely in health care information and communication technology. Clinical validation is one point of such governance, and model testing another. The Plan Do Check Act cycle can be applied for governance of Detailed Clinical Models. Finally, collections of clinical models do require a repository in which they can be stored, searched, and maintained. Governance of Detailed Clinical Models is required at local, national, and international levels.

This chapter describes quality and safety risks related to the development and use of Detailed Clinical Models (DCM) and mechanisms which may be employed to mitigate such risks. The chapter begins with a brief discussion of DCMs and the role they can play in mitigating patient safety risk. There is then a brief description of the risks which DCMs themselves may introduce, followed by the introduction of a standards-based risk assessment method and the ways this assessment method may be applied to DCMs in particular. A general description is then made of the ISO 9000-based approach to quality management systems (QMS) and, specifically, how such an approach may be applied to DCM development, maintenance, deployment and use. The chapter concludes with a discussion of specific DCM quality and safety challenges and governance approaches which may be employed to help address these.

As a basis for semantic interoperability, ideally, a Clinical Knowledge Resource for a clinical concept should be defined formally and defined once in a way that all clinical professions and all countries can agree on. Clinical Knowledge Governance is required to create high-quality, reusable Clinical Knowledge Resources and achieve this aim. Traditionally, this is a time-consuming and cumbersome process, relying heavily on face-to-face meetings and being able to get sufficient input from clinicians. However, in a national or even international space, it is required to streamline the processes involved in creating Clinical Knowledge Resources. For this, a Web 2.0 tool that supports online collaboration of clinicians during their creation and publishing of Clinical Knowledge Resources has been developed. This tool is named the Clinical Knowledge Manager (CKM) and supports the development, review and publication of Clinical Knowledge Resources. Also, post-publication activities such as adding terminology bindings, translating the Clinical Knowledge Resource into another language and republishing it are supported. The acceptance of Clinical Knowledge Resources depends on their quality and being able to determine their quality, for example it is important to know that a broad umber of reviewers from various clinical disciplines have been involved in the development of the Clinical Knowledge Resource. We are still far from realizing the vision of a global repository of a great number of reusable, high-quality Clinical Knowledge Resources, which can provide the basis for broad semantic interoperability between systems. However progress towards this aim is being made around the world.

This chapter describes a middle-out approach to eHealth interoperability, with strong oversight on public health and health research, enabled by a uniform and shared content model to which all health information exchange conforms. As described in New Zealand's Interoperability Reference Architecture, the content model borrows its top level organization from the Continuity of Care Record (CCR) standard and is underpinned by the openEHR formalism. This provides a canonical model for representing a variety of clinical information, and serves as reference when determining payload in health information exchange. The main premise of this approach is that since all exchanged data conforms to the same model, interoperability of clinical information can readily be achieved. Use of Archetypes ensures preservation of clinical context which is critical for secondary use. The content model is envisaged to grow incrementally by adding new or specialised archetypes as finer details are needed in real projects. The consistency and long term viability of this approach critically depends on effective governance which requires new models of collaboration, decision making and appropriate tooling to support the process.

Data governance is characterised from broader definitions of governance. These characteristics are then mapped to a framework that provides a practical representation of the concepts. This representation is further developed with operating models and roles. Several information related scenarios covering both clinical and non-clinical domains are considered in information terms and then related back to the data governance framework. This assists the reader in understanding how data governance would help address the issues or achieve a better outcome. These elements together enable the reader to gain an understanding of the data governance framework and how it applies in practice. Finally, some practical advice is offered for establishing and operating data governance as well as approaches for justifying the investment.

Casemix systems are used in many countries around the world. The reasons for the popularity of casemix systems will be clear once their design features and applications are explained. The specific design issues for acute and other health care settings are discussed, along with their application to paying for care, utilisation review, quality assurance and clinical governance. The quality of the data is important to the integrity of these systems, and the chapter closes with a discussion of the causes of errors in the data and how the quality can be improved.

This chapter is a review of data mining techniques used in medical research. It will cover the existing applications of these techniques in the identification of diseases, and also present the authors' research experiences in medical disease diagnosis and analysis. A computational diagnosis approach can have a significant impact on accurate diagnosis and result in time and cost effective solutions. The chapter will begin with an overview of computational intelligence concepts, followed by details on different classification algorithms. Use of association learning, a well recognised data mining procedure, will also be discussed. Many of the datasets considered in existing medical data mining research are imbalanced, and the chapter focuses on this issue as well. Lastly, the chapter outlines the need of data governance in this research domain.

This chapter examines data quality management (DQM) and information governance (IG) of electronic decision support (EDS) systems so that they are safe and fit for use by clinicians and patients and their carers. This is consistent with the ISO definition of data quality as being fit for purpose. The scope of DQM & IG should range from data creation and collection in clinical settings, through cleaning and, where obtained from multiple sources, linkage, storage, use by the EDS logic engine and algorithms, knowledge base and guidance provided, to curation and presentation. It must also include protocols and mechanisms to monitor the safety of EDS, which will feedback into DQM & IG activities. Ultimately, DQM & IG must be integrated across the data cycle to ensure that the EDS systems provide guidance that leads to safe and effective clinical decisions and care.