Semantic interoperability, e-health and Australian health statistics

Transcription

1 Semantic interoperability, e-health and Australian health statistics Sally Goodenough Abstract E-health implementation in Australia will depend upon interoperable computer systems to share information and data across the health sector. Semantic interoperability, which preserves the meaning of information and data when it is shared or re-purposed, is critical for safe clinical care, and also for any re-use of the information or data for other purposes. One such re-use is for national health statistics. Usable statistics rely on comparable and consistent data, and current practice is to use agreed national data standards to achieve this. The standardisation and interoperability needed to support e-health should also provide strong support for national health statistics. This report discusses some of the semantic interoperability issues involved in moving from the current data supply process for national health statistics to an e-health-enabled future. Keywords (MeSH): Data Collection; Vital Statistics; Data Interpretation, Statistical; Semantics Supplementary keywords: Semantic Interoperability; Statistical Semantic Layer Health care is a complex, information-rich environment, with enormous amounts of information being recorded and used in day-to-day operations. While data and information may be structured differently (for example, in forms, paper medical records or databases), the boundary between them in the real world is porous. Computer and human information systems are closely integrated, and information and data move back and forth across that boundary many times. As information and data move between health systems and their human users, it is important that meaning is not changed. This preservation of meaning as data is shared or transformed is known as semantic interoperability. Computer systems are semantically interoperable if they facilitate the coding, transmission and use of meaning across seamless health services, between providers, patients, citizens and authorities, research and training (Stroetmann 2009). Preservation of meaning is vital, both for safe health care and as the basis for fitness for purpose of any information, such as national health statistics, derived from health data. Semantic interoperability is, therefore, a critical aspect of e-health implementation in Australia. E-health E-health refers to the use of information and communication technologies to support health care delivery, particularly in the area of sharing clinical information between health care professionals. The National E-Health Strategy Summary states that E-Health is the means of ensuring that the right health information is provided to the right person at the right place and time in a secure, electronic form for the purpose of optimising the quality and efficiency of health care delivery (Australian Health Ministers Conference 2008). To achieve this goal, e-health will rely on standardised information and data structures, as well as standard terminologies such as SNOMED CT ). It is important that these components ensure semantic interoperability, so that the meaning of each piece of information or data is always clear and unambiguous. HEALTH INFORMATION MANAGEMENT JOURNAL Vol 38 No ISSN (PRINT) ISSN (ONLINE) 41

2 Semantics for Australian health statistics The information that is needed to organise and plan health services for Australians is collected from administrative systems and surveys and brought together to create useful measures (statistics) for policy makers and managers. It is critical that these statistics are all measuring the same thing, otherwise they cannot be interpreted and are not useful. The solution that has been developed for national health statistics in Australia is for data providers and statistics users to agree on data standards. Current practice includes agreeing on the meaning of each data item as well as using standard naming methods and standard formats. It also means that there needs to be a process for reaching agreement. For health and welfare statistics, Australian governments (Commonwealth, states and territories) have established national standards governance committees that approve these standards. The standards are managed and shared through the Australian Institute of Health and Welfare s online metadata registry, METeOR, and specify the meaning of data to be supplied for statistical purposes, independent of technical implementation. Semantic interoperability is created through data standards recorded as metadata about datasets. How might e-health impact on these current practices? Once implemented across Australia, e-health is likely to expand the opportunities for obtaining health statistics. There should be more data, on more topics, available from many sources, and able to be aggregated as needed in a more timely way than current collection processes allow. An unresolved question is the degree to which current data standardisation and data supply processes might be replaced or streamlined as a result of the standardisation and semantic interoperability of the e-health systems that will supply future data. Any such change will depend on the support that e-health systems can provide for the statistical semantic layers. Statistical semantic layers A statistical semantic layer is a new layer of meaning created when data is re-purposed for a statistical use. Statistical processes create many such layers. The semantics, or meaning, of the original data is built upon when the data is aggregated into datasets, when data cleansing or editing occurs, when data subsets about particular populations of people or events are created, or when statistical methods are applied. More semantic layers are added as each statistical process occurs, often in a series of different environments or organisations along the data supply chain. The data supply chain for statistics travels through many steps, from specification of user requirements through system development, data collection, processing, analysis, dissemination, archiving, and evaluation (Joint UNECE/ Eurostat/OECD Work Session on Statistical Metadata 2008). Australian Institute of Health and Welfare workshops conducted during 2008 recognised that the data supply chain for national health statistics is, in reality, a series of linked parallel supply chains. Many of the steps listed above are repeated over and over as data moves from health delivery settings through regional and state/territory structures before being aggregated nationally (Bluhdorn & Goodenough 2008). This suggests that the statistical semantic layers are built up piece by piece as the data moves along its supply chain. Vertical scope of semantic interoperability Consider the example of a patient who is admitted to hospital because of acute asthma. Existing operational systems manage the information about this event, although the meaning of that data is not necessarily made explicit anywhere. Future e-health enabled systems will standardise the meaning of clinical concepts such as asthma through use of SNOMED CT, and this will form the basis for sharing information about this patient throughout the hospital and across the health sector, such as with the patient s general practitioner. For statistical purposes, the data around this event is well defined under current practice. The METeOR metadata registry describes the agreed meaning of episode of admitted patient care while ICD-10-AM defines the classifications around the diagnosis of asthma. These concepts 42 HEALTH INFORMATION MANAGEMENT JOURNAL Vol 38 No ISSN (PRINT) ISSN (ONLINE)

3 form the basis for new concepts in the statistical semantic layers. Aggregation creates a new semantic layer. A patient with asthma becomes a member of the group (or class) of all admitted patients during a particular financial year with a diagnosis of asthma. This new concept builds on the foundations of the original semantics to create a definition for a new entity, a population, which will be counted using a corresponding aggregated dataset. The semantics of the original data now have an additional semantic layer, which describes this population and its characteristics. Each party involved in creating the aggregated dataset (e.g. all the hospitals and state and territory health departments) contributes to this semantic layer through working with and transforming the data. Other layers may be added on top of this one as statistics are created to measure or describe the characteristics of the aggregated data. Examples might be the proportion of this population who also developed pneumonia, or a description of a significant trend in the number of admissions over time. The statistical semantic layers (Figure 1) include definitions of the meaning of populations of interest (e.g. asthma patients, hospital admission events), counts and measures of things (e.g. how many, what proportion) and Evaluative and interpretive semantics the meaning of evaluations and interpretations of measures e.g. signifi cant increase in the number of hospital admissions Statistical semantics the meaning of measures derived from datasets e.g. weighted average of hospital admissions, age standardised admission rate, total, percentage Dataset semantics the meaning of aggregated data about populations e.g. all data about admitted episodes of patient care for a given fi nancial year for a given state or territory Data semantics the meaning of individual data elements e.g. Episode of admitted patient care admission date Figure 1: Semantic interoperability for statistics: vertical scope interpretive and evaluative information (e.g. the significance or importance of any change, from both statistical and real world perspectives). These statistical semantic layers have their own semantic context, the practice and methodology of statistics (e.g. the use of statistical weightings or the assessment of statistical significance). This context is in addition to the medical context of the original data. Interpreting statistical data depends upon having good information about the meaning of the data at all stages in the statistical life cycle. Much of this information exists in the statistical semantic layers as metadata, and relates to aggregated datasets derived from the original data. It will not be enough to have e-health standardised data at the operational level. Unless we also have good quality, accessible metadata describing what else has happened to that data and how it has been aggregated and manipulated, there will be unanswered questions about the meaning of statistics based on that data. Horizontal scope of semantic interoperability To support national health statistics, semantic interoperable computer systems will therefore need to connect vertically with the statistical semantic layers. Semantic interoperability also needs to extend horizontally, beyond the boundaries of clinical systems. E-health has a strong focus on clinical healthcare. However, healthcare delivery includes not only clinical activities but also integrated supporting processes such as administration, legislative requirements, reporting, and financial reimbursement. This means that e-health standards will need to include semantics, not only for clinical concepts such as blood pressure, but also for administrative concepts such as treatments or admissions. Some relevant concepts may also be defined externally in legislation or regulations (such as the Medicare Benefits Schedule) or by international agreement (such as reporting using the International Classification of Diseases). Meanings need to be harmonised across these distinct but inter-dependent domains (Figure 2). It is also very important for meaning to be described in lay terms so that it is accessible to the general public. If the meaning of data or HEALTH INFORMATION MANAGEMENT JOURNAL Vol 38 No ISSN (PRINT) ISSN (ONLINE) 43

4 Clinical Health administration Financial Legal and reporting information and data information and data information and data information and data Figure 2: Semantic interoperability for statistics: horizontal scope terminology can be understood only by medical professionals, the statistics based on them will not be meaningful to the whole community, and will not inform policy and planning as well as they could do. Implications for health IT systems For e-health to be effective there must be semantic interoperability between all the clinical systems involved in health delivery. For optimal statistical re-use of that clinical data for policy and planning purposes, semantic interoperability needs to expand horizontally to include administrative systems, since health care does not happen in a vacuum but within an administrative, legislative and financial structure. For that statistical re-use to be of sufficient quality to be fit for purpose, there is also a need for semantic interoperability to expand vertically to connect with and inform the statistical semantic layers that build on the original data. If this degree of semantic interoperability is to be achieved, there must be explicit, understood shared meaning that is linked to data as it is shared or re-purposed. If any components of meaning are hidden within computer systems and software, those components must be exposed so that their effects on the meaning of data are clear. This applies both to clinical and administrative systems that support health delivery and to statistical systems that re-use data from those systems. Rigorous end-to-end management, accessibility and quality control of metadata will also be needed. The statistical users of data need to be confident that the right metadata and right meaning is attached to the right piece of data, and be able to find out what has happened to each piece of data during its journey from the clinical system into national health statistics. Future health IT system development will need to include support for semantic interoperability, but many of the building blocks are still in a state of flux. The European Union SemanticHEALTH project report (Stroetmann 2009) provides a comprehensive overview of the current state of play and recommends a roadmap for ongoing development. Conclusion Semantic interoperability must exist in e-health systems if they are to support clinical care. However, this is unlikely to be sufficient to support national health statistics unless semantic interoperability is extended to include administrative systems, the statistical semantic layers, and downstream data and metadata stores. Establishing widespread semantic interoperability is a key information management challenge for the future. However, as a starting point, health system design should consider the need for accessible, managed metadata to support downstream re-uses of the data in those systems through the use of data dictionaries or metadata registries with robust change management and quality control processes that support the statistical semantic layers. References Australian Health Ministers Conference (2008). National E-Health Strategy Summary December Melbourne, Victorian Department of Human Services, on behalf of the Australian Health Ministers Conference. Available at: National%20E-Health%20Strategy.pdf (accessed 03 March 2009). Bluhdorn, M. and Goodenough, S. (2008). The impact of ehealth on Australian statistics. Proceedings of the 2008 Symposium of the Health Information Management Association of Australia, September, 2008, Canberra. North Ryde, N.S.W., Health Information Management Association of Australia. 44 HEALTH INFORMATION MANAGEMENT JOURNAL Vol 38 No ISSN (PRINT) ISSN (ONLINE)

5 Joint UNECE/Eurostat/OECD Work Session on Statistical Metadata (METIS) (2008). Generic Statistical Business Process Model Version 3.1 December Geneva, United Nations Economic Commission for Europe Secretariat. Available at: documents/ece/ces/ge.40/2009/mtg1/wp.2.e.pdf (accessed 09 March 2009). Stroetmann, V.N. (Ed.), Kalra, D., Lewalle, P., Rector, A., Rodrigues, J.M., Stroetmann, K.A., Surjan, G., Ustun, B., Virtanen, M. and Zanstra, P.E. (2009). Semantic Interoperability for Better Health and Safer Healthcare: Deployment and Research Roadmap for Europe. Luxembourg, Office for Official Publications of the European Communities. Available at eu/information_society/activities/health/docs/ publications/2009/2009semantic-health-report.pdf (accessed 03 March 2009). Sally Goodenough BA(Hons)(Psychology), BEc, BA(Asian Studies), GradDip Library and Information Management, AALIA Tel: sally.goodenough@internode.on.net Previously: Australian Institute of Health and Welfare Views expressed in this report are those of the author. HEALTH INFORMATION MANAGEMENT JOURNAL Vol 38 No ISSN (PRINT) ISSN (ONLINE) 45