Concept and objectives of the ELFis information system

The principles respected in the design and implementation of ELFis are as follows:

  • International relevance and best practice methodological standards. The design of the information system is derived from best practices recommended by major international studies and methodologies. In the questionnaire part, which assessed the attitudes of the medical team, the patient and their relatives, internationally used and recommended questionnaires IPOS, NECPAL-CCOMS and VOICES were tested and adopted. The whole solution is based primarily on administrative data; the questionnaire surveys are ready for detailed data completion or for implementing different types of studies.
  • Efficient modular architecture of the system. The entire ELFis concept is built modularly to allow, in particular, the enrichment of patient documentation with relevant quality indicators, physicians' opinions and predictions, and patient attitudes. The concept aligns with the methodological recommendations formulated by the major international projects CANCON and iPAAC in building the information base for palliative care. Reports over central data sources are embedded in the overall infrastructure tracking diagnosis and processes in providers' administrative data.
  • Patient-centredness is the orientation towards the patient, whose previously expressed wishes and feedback are crucial, especially in end-of-life care. For these reasons, the ELFis proposal includes (a) questionnaires assessing the patient's attitude and evaluation of the course of care, including the wishes expressed, and (b) the VOICES questionnaire, which allows for the retrospective evaluation of care after death by the deceased's loved ones (the concept of 'follow-back' monitoring). The centralised reports focus primarily on healthcare providers, especially hospitals, and include an essential component, the monitoring of patient-centred processes, including hospital palliative care teams (HPCT). The automated data collection system resulting from HPCT interventions and the subsequent monitoring of patient trajectories and treatment outcomes makes ELFis a truly individualised information system.
  • A high degree of data protection and privacy of data subjects. Data collections are designed in a format where only anonymous data, equipped with an anonymous case ID, are processed centrally. ELFis does not build any central registry with patients’ personal data. This is achieved by ensuring that all data collections are linked to the operations of specific healthcare providers and primary data are collected as a legitimate part of patients’ medical records. Therefore, ELFis does not foresee the need to conduct a specific form of informed consent, as the data subject provides, for example, quality of care assessments voluntarily as part of the assessment conducted by the hospital palliative care team (HPCT). This team also records the results in the healthcare provider’s information system and does not pass on patients’ personal data anywhere else. The same applies to feedback from the family and loved ones of the deceased. The opt-in mode is strictly followed in all surveys involving patients or their relatives. The participating healthcare facilities make all entries into the ELFis components internally, i.e., without linking to external software or data repositories. After successful implementation (installation) of ELFis tools in the hospital environment, the processor leaves the system and has no access to specific patients' primary data and health records. The primary data layer is thus separated from the layer aggregating the various reports and the analytical layer.
  • Standards corresponding to observational clinical studies. The implementation of data collection was already equipped with a protocol during project testing that met the standards of high-quality observational studies. ELFis retains this attribute for ongoing data processing, for which it additionally offers 100% coverage of the target subject population with administrative data.
  • Barrier-free implementation and long-term sustainability. The system respects the standard rules of operation of hospital palliative care teams as much as possible, and its design does not plan to introduce administratively burdensome steps.
  • Usability in education. The statistical outputs of ELFis (reporting over aggregated data) are projected into retrospective reporting that serves as feedback to improve the quality of provided care and as a basis for education.

Basic characteristics of the ELFis system

The system:

  • will be easy to implement in the operation of healthcare facilities and will not interfere with the work of the hospital palliative care team;
  • will not interfere with the health services provision in the healthcare facilities concerned and will not change in any way the way and format of reporting care to health insurance companies;
  • will not lead to any changes in the content and scope of the lege artis care provided;
  • will store and archive data primarily collected by hospitals on their internal repositories and will be fully subject to the cybersecurity and data protection rules of each hospital;
  • will use clinical data and records already existing (and routinely generated) in hospitals; these data and records on the course and quality of care cannot be readily obtained from the hospitals' existing administrative resources;
  • will combine local analysis of hospital administrative data with questionnaire surveys (central data) carried out by hospital experts, which are consistent with international experience and standards;
  • will provide local data attributable to individual teams but will also allow for comparison or generalisation of findings at the regional level, based on the analysis of aggregated and irreversibly anonymised data;
  • will cover all components and target groups of the end-of-life care system, i.e. provider/founder – doctor / medical team – patients.

The new version of the ELFis system strictly separates the primary provider data layer from the analytical and reporting layers. This strengthens the security of the system and the protection of patients’ personal data. Primary data, i.e. those entered by participating providers into the ELFis databases, must not be passed on to third parties or otherwise directly published and made public without the provider’s consent. The provider has full access to its primary data entered into the ELFis system. While the provider does not and cannot have full access to the primary data of other healthcare providers, it does receive data in analytical report format on patient trajectories in the system before and after the care it provides.

The tangible outputs of the ELFis project are, in particular:

  • outputs from the pilot phase testing of the NECPAL, iPOS, and VOICES questionnaires and implementation packages for these data collections;
  • tools for extracting the necessary data collections within health facilities and reporting them to central data repositories and registries;
  • a set of data summaries and open datasets (reports) for Vysočina Region hospitals over ELFis modules with data covering the period (2010) 2013 -> 2023;
  • system for downloading and implementing these resources in the internal environment of hospitals;
  • ELFis portal section with the possibility of interactive user input (design of new data summaries and datasets);
  • an analytical study covering all key dimensions of project implementation, including an evaluation of the activity and effect of hospital palliative support teams. The attached analytical report focuses in particular on the following priority areas for assessment:
    • provision and availability of palliative care in the region, benchmarking within the country
    • organisation of palliative care, provider infrastructure 
    • volume of care
    • staffing – full-time equivalents for doctors and nurses
    • cooperation with social services providers, such as homes for older people and respite care
    • monitoring quality and outcomes of care
    • prediction of capacity needs