Adopting national nursing data standards in Canada

May/June 2017   Comments

Over the past two decades, government and provider organizations throughout the federal, provincial and territorial health-care systems have invested heavily in the acquisition and deployment of health information systems, including electronic health records (EHRs). By virtue of nurses being the largest constituency of health professionals in Canada, they are also the predominant users and contributors of clinical data. To optimally leverage the investments to date and going forward, the timing is right for Canadian nurses to develop a national strategy for the use of technology and informatics. Such a strategy will enable them to evaluate the quality and impact of nursing care, promote safe quality patient care, expand nursing knowledge and support health-system use of nursing data.

While significant investments in EHRs have been made in every Canadian jurisdiction, little effort has been made to unify approaches to online clinical documentation. The opportunity to harmonize the documentation gathered by nurses across organizations (e.g., agreement on basic common admission and discharge assessment information) is being missed by each health-care organization adopting its own format and content. Ironically, potential harmonization is one of the greatest advantages to be derived from the use of EHRs, but it has not been addressed in clinical documentation implementations for nursing or in other health professions.

In 1992, nurses in Canada reached consensus on the data elements required to understand the impact of their practice: client status, nursing interventions and client outcomes (NMDS Conference & Canadian Nurses Association [CNA], 1993). In addition, they identified the need for unique nurse identifiers (a unique number for each nurse in Canada) and nursing resource intensity information (nursing workload) to represent nursing practice in Canadian health-care systems. While there has been progress made in different areas to identify, define and standardize nursing data within these identified data elements, neither the elements themselves nor the data are consistently collected or widely integrated into EHRs. Furthermore, these data are not captured within administrative systems or abstracted into key national data repositories, such as those at the Canadian Institute for Health Information (CIHI).

There is an intensified focus on the need for clinical data standards and interoperability of clinical information systems (Naylor et al., 2015). Many health-care leaders fail to understand the value of standardized data within individual organizations and across care settings. Because of an increased focus on primary health care and on management of chronic illness, there is a need to collect standardized clinical information to support patient transitions and examine clinical outcomes as people transition across the health-care continuum (e.g., from acute care to long-term care and home care).


Glossary of terms

C-HOBIC Canadian Health Outcomes for Better Information and Care
ICNP International Classification for Nursing Practice
interoperability sharing of information across sectors/systems
interRAI International Resident Assessment Instrument
LOINC Logical Observation Identifiers Names and Codes
NNQR(C) National Nursing Quality Report for Canada
SNOMED CT Systematized Nomenclature of Medicine – Clinical Terms

Data standards, clinical outcomes and EHRs

Veillard, Fekri, Dhalla and Klazinga (2015) cited the need for outcome measures to support clinical decision-making and aid in understanding how effective the health system is in achieving its goals. Capturing health-care data, including nursing data, in a structured way is essential to “accomplish the vision of accurate, reliable, clinically meaningful measurement across systems and settings of care” (Healthcare Information and Management Systems Society, 2015). National and jurisdictional endorsements of data and documentation standards such as ICNP, interRAI, LOINC and SNOMED CT have set the stage for broader adoption of standards. Specific nursing initiatives such as NNQR(C) and C HOBIC have begun to enable the standardized collection of nursing data within specific jurisdictions and health-care organizations. These initiatives move beyond the collection of standardized information on disease and disability to the capture of standardized clinical patient outcomes that are of value to clinicians in their practice and important to people within the health-care system.

NNQR(C). The NNQR(C) was an initiative of Canada Health Infoway, CNA and the Academy of Canadian Executive Nurses (ACEN). Their goal was to develop a coordinated system to collect, store and retrieve nursing data in settings across Canada. The project collected information on structure, process and outcome indicators from 10 pilot sites, to assist in evaluating the care being provided in health-care sectors, with a long-term goal of establishing a sustainable national nursing quality report.

The pilot sites included medical/surgical units, adult post-acute and rehabilitation units, and long-term care and mental health units. Unit-level nurse staffing structural indicators and processes of care data, as well as nursing-sensitive outcomes measures, were submitted on a quarterly basis (VanDeVelde-Coke, Doran & Jeffs, 2015). The NNQR(C) initiative also included some primary data collection in a survey of nurse job satisfaction and work environments. Some of the key findings, based on the overall results, showed that more nursing hours were associated with improved aggressive behaviour scores, lower use of restraints and appropriate hand hygiene practices. Fewer nursing hours were associated with high falls rates. Higher pain scores were associated with higher percentage of patients restrained. One of the challenges encountered was that few of the data required for this study were collected or reported consistently and were not available electronically.

C HOBIC. The C HOBIC initiative was originally funded with contributions from Infoway and was sponsored by CNA. The focus for this work is the collection and use of a set of patient outcomes sensitive to nursing care (Hannah, White, Nagle, & Pringle, 2009). C HOBIC introduces a systematic, structured collection of patient assessment data, enabling this information to be coded (using standardized clinical terminology), abstracted into jurisdictional EHRs and aggregated to make it available to clinicians across the health-care system. This data set has been endorsed by the Infoway Standards Collaborative as a Canadian Approved Standard.

Initially, the focus was on implementation of this data set in three provinces (CNA, 2009). A subsequent focus was on facilitating patient transitions from one sector of the health-care delivery system to another through the provision of a synoptic report to support the sharing of clinical information among clinical disciplines and care settings (CNA, 2015). Findings from the evaluation confirm the value of the C HOBIC information in supporting care transitions. However, it was clear that more effort needs to be directed to strengthening the processes of information exchange between care providers across the continuum and working with clinicians on using data such as C HOBIC to inform practice (CNA, 2015).

Efforts are currently underway to include the C HOBIC data set in the acute care Discharge Abstract Database (DAD) at CIHI (2016b). This will provide standardized patient-centred clinical outcomes data from acute care to support aggregation and analysis of clinical outcomes, health system use and performance reporting for local, provincial and national analysis and use.

Demonstration projects are being conducted at Grey Bruce Health Services in Ontario and St. Boniface Hospital in Manitoba, using two clinical information system vendors (Cerner and Allscripts) and two abstracting vendors (3M and MED2020). Data collection will occur for six to eight months, and an evaluation will be undertaken to provide the C HOBIC team, CIHI and provincial/territorial partners with feedback on the feasibility, value and utility of including this data set in the DAD. For the first time, clinical data contributed by nursing will be included in the DAD.

Developing an action plan

The goal of a symposium attended by more than 60 nursing and health-care leaders in April 2016 was the development of an action plan for moving forward on the collection and use of nursing data standards (see “Strategizing for National Nursing Data Standards,” in the November 2016 issue). A similar event was planned for April 2017, bringing together leaders in clinical practice, clinical administration, education, research and policy.

Clinical practice. Numerous efforts have been made to bring evidence to nurses in practice settings and support them in using the information they are gathering to make clinical decisions. Best practice guidelines/pathways, smartphone apps (e.g., drug manuals, calculators), point-of-care documentation tools (e.g., bar-code readers), and access to Internet resources can facilitate and support evidence-informed practice. It is important for health-care delivery organizations to consistently enable and support evidence-informed practice within and across the health-care system. Moreover, in conjunction with the adoption of standardized data and documentation methods, large volumes of comparable clinical data become available for analysis and study, thereby facilitating the generation of new knowledge and evidence more rapidly.

Nurses need information systems to include the capture of information necessary for them to demonstrate appropriate clinical actions based on data gathered through the processes of care (e.g., are they adequately preparing patients for discharge from hospital to home?). Documentation standards should encompass the use of standardized nursing data and evidence-based tools to guide assessment, interventions, clinical decision-making and outcomes evaluation.

Clinical administration. Nurse leaders recognize the value of balanced scorecard reporting that encompasses financial, patient and staff measures to support decision-making (e.g., staffing, budgeting, program design, models of care). Standardized data is essential for effective, evidence-informed decision-making. In addition, standardized data allows nurse leaders to look across the system to better understand the components of operations that drive outcomes. If organizations are measuring the same things in the same way, they can determine where things are working (e.g., new staffing models) and where there are opportunities for improvement.

Education. New nursing graduates and the existing nursing workforce need to be informatics savvy. Further development of nursing expertise in informatics is needed, particularly related to standardized terminologies and standardized clinical documentation tools. The Canadian Association of Schools of Nursing (2012) published entry-to-practice informatics competencies for registered nurses. To date, efforts have been directed to engaging nursing faculty to advance their understanding and approaches to integrating these competencies into undergraduate nursing curricula, but it is still early days. The existing nursing workforce has been largely exposed to the use of information and communication technologies in practice settings. However, the use of EHRs in health-care settings does not equate to informatics competency, particularly as it relates to the use of standardized nursing data and documentation to inform clinical nursing judgment and the use of evidence derived from standardized data and documentation.

Research. The Canadian informatics research community remains limited when it comes to research related to the adoption and use of standardized terminologies. However, there is a growing body of research focused on the use of standardized nursing-sensitive outcomes. A study that examined the C HOBIC admission data set as a predictor of alternative level of care and length of stay found that higher fatigue and dyspnea scores on admission were significantly related to longer lengths of stay. Furthermore, patients with high scores for fatigue, a history of falls and, to a lesser extent, a high activities of daily living composite score on admission were more likely to be discharged to either complex continuing care, long-term care homes or rehabilitation facilities (Jeffs et al., 2012). Research linking the C HOBIC data set to other data sets held at CIHI found that therapeutic self-care scores showed a consistent and significant protective effect for readmission to acute care at 7, 30 and 90 days; nausea was more strongly related to early readmissions (3, 7 and 30 days) and dyspnea was more strongly related to readmission at later stages (30 and 90 days) (Wodchis, McGillis Hall, & McQuigley, 2012). A home care study highlighted the importance of assessing therapeutic self-care to protect against hospital readmissions and other adverse events (Sun & Doran, 2014; Sun, Doran, Wodchis, & Peter, 2017).

Nonetheless, additional research is essential to further understand the potential impact and benefit of data standards for practice (i.e., clinical outcomes [patient, quality, safety], nurse outcomes [nurse burnout, job dissatisfaction and intentions to leave the profession] and health-services administration as it relates to resource management and service delivery). As depicted in the figure (Nagle & White, 2015), all levels of the health-care system would benefit from additional research focused on the convergence of standardized, abstracted and aggregated clinical data that can be studied relative to other individual, local, regional and national data sets.

Standardized Data — Collected Once, Used for Many Purposes. Examples of data collected, abstracted, aggregated, analyzed at the individual/case mix group level: assessments, interventions, outcomes, provider, hours of care, adverse events and cost of care. These data inform: safety and quality, accountability, outcomes and evidence. Examples of data collected, abstracted, aggregated, analyzed at the organization/sector level: case volumes, outcomes, cost of care and resource utilization. These data inform: safety and quality, resource management, funding, accreditation, public reporting and research. Examples of data collected, abstracted, aggregated, analyzed at the regional/jurisdictional level: disease incidence and prevalence, outcomes, cost of care and resource utilization. These data inform: health policy, legislation, health system performance, funding, public reporting and research. Examples of data collected, abstracted, aggregated, analyzed at the national level: comparative disease incidence, prevalence and trends, resource utilization. These data inform: health policy, legislation and research.

Policy. As shown in the figure (Nagle & White, 2015), the availability of aggregated, standardized data and information will inform health policy directions related to the distribution and use of nursing resources by type, within specific sectors and for specific populations. These data will significantly broaden the understanding of provider organizations and regional, jurisdictional and national policy-makers about health system performance. The availability of comparative data and information for benchmarking, public reporting and transparency is of increasing importance in terms of perceived value for investment in health services. Accountability for clinical and financial outcomes will be better understood relative to health human resources use in all sectors. C HOBIC data can be easily correlated with many other important metrics such as staffing, cost of care, length of stay and readmission rates. These analyses can help an organization assess performance and clinical outcomes and answer questions about the impact of nursing practices on broader clinical practice (CIHI, 2016a). The contributions of nurses to these outcomes warrant much greater clarification. This will be realized only with the adoption of national nursing data standards and reporting in practice settings nationwide.

The importance for nursing

According to a 2015 report from the Healthcare Information and Management Systems Society, “if nursing fails to process nursing care data in an electronic format, healthcare decisions will be made without nursing input.” In Canada, there were more than 390,000 nurses employed in a regulated nursing profession in 2015 (CIHI, 2016c). Therefore, nursing must ensure the information they collect is standardized, can be shared with other providers, organizations and patients and is abstracted into data repositories to facilitate research and health policy decision-making.

As professionals, nurses are accountable for their practice. What information are they using to demonstrate their accountability? The collection of information in a standardized format, along with real-time reports displaying changes in outcomes information from admission to discharge and/or across the continuum, can assist members of the profession in demonstrating the role of nurses in, and their contributions to, health outcomes. Nurses themselves will understand what practices lead to improved outcomes for the people they provide care for.

As more information becomes available in EHRs and the use of mobile health apps increases, patients will be entering the health-care system with their own health information. Nurses need to be able to process and use this information in planning and evaluating care to ensure that their full scope of practice is realized.

With the advent of new EHR implementations, the design of online clinical documentation and support for the adoption of standardized clinical data by organizations such as CNA, CIHI and Infoway, the time is right for articulating a national strategy to unite Canadian nursing in representing, teaching, capturing and reporting practice. Further, a unified clinical data strategy will support the study and advancement of nursing practice and health-care policy that will, in turn, strengthen the quality and safety of clinical care. It has long been recognized, “If we cannot name it, we cannot control it, finance it, teach it, search it or put it into public policy” (Clark & Lang, 1992).

As the expansion of EHRs continues, it is important that the collection of minimum data sets is included to allow for pan-Canadian comparisons (Veillard et al., 2015). It is vital that information about nursing practice be included in EHRs and data repositories to inform practice, research and health policy. Furthermore, all health-care organizations should be focused on providing standardized clinical information to clinicians in real time and working with them to use this information to inform and evaluate their practice — an essential requirement for a transformed health-care system.

For more information and to find out how to support this work, visit cna-aiic.ca/informatics.


References

Canadian Association of Schools of Nursing. (2012). Nursing informatics entry-to-practice competencies for registered nurses.

Canadian Institute for Health Information. (2016a). CIHI’s annual report, 2015-2016: Charting a new course.

Canadian Institute for Health Information. (2016b). Inclusion of nursing-related patient outcomes in electronic health records [Information sheet].

Canadian Institute for Health Information. (2016c). More regulated nurses entering the profession than leaving it.

Canadian Nurses Association. (2009). Inclusion of nursing-related patient outcomes in jurisdictional electronic health records: Canadian Health Outcomes for Better Information and Care (C-HOBIC) Final Report.

Canadian Nurses Association. (2015). Canadian Health Outcomes for Better Information and Care: C-HOBIC Phase 2 Final Report.

Clark, J., & Lang, N. (1992). Nursing’s next advance: An international classification for nursing practice. International Journal of Nursing, 39(4), 102-112.

Hannah, K. J., White, P., Nagle, L., & Pringle, D. M. (2009). Standardizing nursing information in Canada for inclusion in electronic health records: C-HOBIC. JAMIA, 16(4), 524-530. doi:10.1197/jamia.M2974

Healthcare Information and Management Systems Society. (2015). CNO-CNIO Vendor Roundtable: Guiding principles for big data in nursing.

Jeffs, L., Jiang, D., Wilson, G., Ferris, E., Cardiff, B., Lanceta, M.,…Pringle, D. (2012). Linking HOBIC measures with length of stay and alternate levels of care: Implications for nurse leaders in their efforts to improve patient flow and quality of care. Nursing Leadership, 25(4), 48-62. doi:10.12927/cjnl.2013.23263

Nagle, L. M., & White, P. (2015). Towards a Pan-Canadian Strategy for Nursing Data Standards. Unpublished white paper.

Nagle, L. M., & White, P. (2016). National Nursing Data Standards Symposium Proceedings. Final report submitted to Canadian Nurses Association, Canadian Institute for Health Information, Canada Health Infoway.

Naylor, D., Girard, F., Mintz, J., Fraser, N., Jenkins, T., & Power, C. (2015). Unleashing innovation: Excellent healthcare for Canada — Report of the advisory panel on healthcare innovation.

NMDS Conference & Canadian Nurses Association. (1993). Papers from the Nursing Minimum Data Set Conference: 27-29 October, 1992.

Sun, W., & Doran, D. (2014). Understanding the relationship between therapeutic self-care and adverse events for geriatric home care clients in Canada. Journal of the American Geriatrics Society, 62(Suppl. 1), 1-7.

Sun, W., Doran, D. M., Wodchis, W. P., & Peter, E. (2017). Examining the relationship between therapeutic self-care and adverse events for home care clients in Ontario, Canada: A retrospective cohort study. BMC Health Services Research, 17, 206. doi:10.1186/s12913-017-2103-9

VanDeVelde-Coke, S., Doran, D., & Jeffs, L. (2015). Update on the NNQR(C) pilot project. Canadian Nurse, 111(2), 10-11.

Veillard, J., Fekri, O., Dhalla, I., & Klazinga, N. (2015). Measuring outcomes in the Canadian health sector: Driving better value from healthcare. C.D. Howe Institute Commentary.

Wodchis, W., McGillis Hall, L., & Quigley, L. (2012, February). Increasing patient self-care to avoid acute care readmissions. Unpublished data presented at the HOBIC Symposium: Demonstrating Value with HOBIC Data. Toronto, ON.

Peggy White, RN, MN

Peggy White, RN, MN, is project director for C HOBIC and president of the Canadian Nursing Informatics Association.

Lynn Nagle, RN, PhD

Lynn Nagle, RN, PhD, is assistant professor, Lawrence S. Bloomberg faculty of nursing, University of Toronto.

Kathryn Hannah, CM, RN, PhD

Kathryn Hannah, CM, RN, PhD, is national executive lead for C HOBIC and health informatics advisor to the Canadian Nurses Association.

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