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Introduction
Persisting systems gaps and fragmentation of care within and between health care teams in primary and secondary care are the key issues being addressed by most national health systems, examples being the Australian primary health care (PHC) strategic framework,1 NSW State Health Plan2 and eHealth blueprint.3 The long-suffering patient has to deal with fragmented roles and responsibilities compounded by poor communication, fragmented programs and services. This fragmentation extends across interfaces for care, information provision, identification and handover, guidelines use and standards, access to their own records and privacy compliance.4,5 Fragmentation leads to poor and unsafe care and inappropriate use of emergency and hospital services, which are often preventable. Continuity and coordination of patient-centred care requires effective communication, coordination, teamwork and judicious use of information and communication technology4,5 within a medical home6 and across the health neighbourhood.7,8 This requires the effective use of electronic health records (EHRs), personal health records and electronic decision support tools to collect, share and use good quality information to provide safe, effective and coordinated care along ‘patient journeys’ and ‘care pathways’ in the health and social care system. However, while there is consensus about how EHRs and informatics structures and processes support good quality care, there is less agreement about implementation.9
Observational EHR data are also increasingly being mined, linked, aggregated, cleaned and used for audit, continuous quality improvement, health service planning, surveillance and epidemiological study, evaluation research and measurement and monitoring of quality of care, particularly of patients with chronic diseases. Fundamental questions about observational EHR data include:
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What are the data types?
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How good are the data and metadata?
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How good are the data tools and systems?
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Is the information fit for purpose?
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Can poor data lead to poor care?
Despite improved informatics capability, data quality (DQ) in primary care10 and hospital systems11 has serious deficiencies. However, ontological data management methods can maximise its potential and ensure as far as possible fitness for purpose.12,13 Tools to collect/extract data and assess and manage DQ are often inconsistent and should be validated in a transparent manner within a robust data and clinical governance framework.11,14 There is also a de facto failure among primary and secondary care clinicians to share data beyond the traditional referral letters, clinic letters and traditional structured documents such as discharge summaries.15 Key issues and research questions exist around whether there are causal relationships between good data and good care, inter-professional relationships, information-sharing, health literacy and patient engagement/empowerment.
This paper describes our vision of an integrated health neighbourhood (IHN) underpinned by a community wide health information exchange (HIE) using a model of multilevel integration of data, information, clinical practice and disciplines to support inter-professional coordinated care across primary, secondary and tertiary care settings. The care must be safe, evidence-based, continuous, coordinated, accessible and equitable. We see the IHN as a learning organisation using complex collections of EHR data optimally for service provision, education and research. The data presented in this paper form part of the evaluation of the informatics infrastructure – HIE and data repository – for its reliability and utility in supporting the IHN.
Context for the vision
The context includes the regulations, policies, strategies and resources available to governments, funders, planners and implementers to achieve the mission of equitable access to well-coordinated safe and evidence-based care.
Primary health care framework: The Australian PHC strategic framework1 describes:
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a consumer-focused integrated PHC system;
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improved access and reduce inequity;
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health promotion, prevention, screening and early intervention;
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quality, safety, performance and accountability.
Interactive model of access and equity: Access is an interaction and a balance of demand and supply.16 Demand includes the burden of disease and attributes of the population such as knowledge, attitudes, skills, capacities and self-care practices. Supply includes location, availability, cost and appropriateness of the services. The supply and demand provider-user dyads include:
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approachability – ability to perceive;
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acceptability – ability to seek;
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availability/accommodation – ability to reach;
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affordability – ability to pay;
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appropriateness – ability to engage.
Access and equity is further complicated by increasing health costs and demand arising from increased expectations of health and the ageing population with increased morbidity.
Purpose of EHR and information systems: EHR data would be used for:
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accurate identification of an individual;
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supporting managerial functions and integration of data and systems;
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supporting clinical care and coordination;
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supporting care of populations;
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supporting research, evaluation and audit to inform DQ, governance, business models, policy and practice;
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creating a real-world evidence base around the management of complex comorbidity and frailty in an ageing population.17
Expanded chronic care model: The chronic care model (CCM) (Figure 1) incorporates the policy, clinical and managerial systems and processes that must be in place to provide safe, effective and coordinated care to individuals and populations.18 The health care team needs to be activated and proactive to engage meaningfully with the activated and empowered patient to guide them through the health system to ensure access to and appropriate use of health services. This requires the integration of data/knowledge, clinical service and inter-disciplinary teamwork across the health neighbourhood.
Multilevel integration to support care: Figure 2 summarises the key levels of integration and the standards required at each level:
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data and information system;
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knowledge and evidence;
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clinical services within the medical home;
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inter-disciplinary team work across the health neighbourhood.19
The vision is an IHN and an information enhanced learning organisation.
Integrated health neighbourhood: For translational and health services research, the IHN is the logical unit of data collection and denominator for data analysis. With the understanding of general practices as medical homes, the health neighbourhood is described as the actor network and information ecosystem20 created by secondary care health and hospital services and the surrounding primary care, community health and general practice services.
The complexity of the EHR data collected from the IHN includes time and space dimensions, which we believe will require innovative ontology-based approaches to optimise their use.12 The IHN concept needs record linkage to enable the tracking of individual patients across different services in the IHN. This also enables the elimination of duplicates in prevalence/incidence studies. The Fairfield IHN (FIHN) uses GRHANITE™ for probabilistic record matching and linkage through pseudonymns created by the hashing of personal identifiers.21
Case study: The Fairfield IHN
Methods: We established the FIHN (Figure 3) with support from the local hospital/health services and the local UNSW general practice-based research network. The informatics infrastructure – an electronic data repository, which links and manages data extracted from the Fairfield hospital admissions, emergency department and diabetes clinic (
) and ten general practices (
) in the Fairfield local government area – was installed.
Standard operating procedures are in place for DQ management and governance to ensure compliance to quality, privacy and security standards. We focused initially on DQ assessment and management,13 validation of data extraction14 and management tools,10,11 and exploring the use of ontologies to collect, link, manage, store and analyse complex personal clinical data from these multiple disparate EHRs.22,23 The FIHN database architecture was established with ontology-based data access, using tools like SPARQL, to complement relational database access22 with algorithms to create cohorts and to integrate data, information, practice and disciplines in the medical home and across the IHN. This work is ongoing to establish automated protocols to improve the DQ, through structured DQ reports (SDQRs) and feedback,24 to enable accurate epidemiological and cohort studies.
Findings: In the following section, we describe examples of basic, informatics and applied projects to illustrate the systematic approach to developing and testing the tools to establish an informatics infrastructure to achieve this vision.
Example: Phenotyping and case finding: The accuracy of the FIHN phenotyping algorithm for Type 2 diabetes mellitus (T2DM) in the data repository was assessed using both biostatistical (sample size) with ontological (combination of attributes) methods.22 In addition to acceptable accuracy of the case finding tool, we also confirmed the common understanding that accuracy increased with increased sample size and increased number of attributes used in the query (Tables 1 and 2).
Comment: At this point, we would not recommend unsupervised use of the data for patient care. With some caveats on the DQ, especially the completeness and representativeness, the data repository can be used for research, education and quality improvement purposes.
Example: Record linkage and patient journeys: The FIHN pseudonym-linked datasets21 enable the tracking of patients journeys through primary care services in the neighbourhood (Table 3). This shows there is significant number of patients using a number of general practices in the neighbourhood, usually for access reasons. On the other hand, it could be an indication of doctor shopping.
Similarly, patient journeys can be tracked through primary and secondary care services in the FIHN through linkage of general practice, diabetes outpatient and hospital data sets (Figure 4). We have defined a cohort of 5186 patients with T2DM (estimated prevalence of 3.5%) in the ten general practices (Nall ages = 151,616). There were 5049 in the diabetes clinic, of these 2428 had at least one admission into the local hospital.
Developments are ongoing, using ontology-based methods, to embed an assessment of temporal and spatial variations in the patient journeys across the health system. This enhancement will improve the relevance and validity of the data for modelling, predictive and logitudinal studies.25
The data linkage is highly accurate as tested by both computer-based and manual (i.e. checking with the general practice) methods. The main problem was similar and sound-alike names, especially with some ethnic communities in the FIHN, one of the most multicultural local government areas in Australia.
Example: Integrating data quality assessment: Ontology-based methods have been developed to embed an assessment of DQ in the management and analysis of the FIHN data repository; it has been shown to be feasible and accurate.23 The accuracy and robustness of the model is currently being refined conceptually and testing with larger data sets, including natural language processing of text data.26–28
Example: Integrating the patient dimension: This is being implemented with feedback and reflection at the professional and practice organisation levels, using practice-focused and patient-centred SDQRs as the basis for discussion. This is a RACGP-recognised quality improvement and professional development exercise, in collaboration with patients. Neighbourhood-constrained online strategies are being developed to engage patients and their carers in their care.
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