Perspectives of Technology Users on the Eve of Innovation Deployment: the Case of Integrated Care Pathways (ICPs)
Bibliographic record
Abstract
Background: Over the last 10 years or so, integrated service networks (ISNs) for older people have been implemented throughout the Province of Quebec in Canada. The main instruments used in the ISNs are the Multiclientele Assessment Tool (OEMC; French acronym), including the Functional Autonomy Measurement System (SMAF); the Iso-SMAF profile classification system; and the Individualized service and allocation plan. All of these instruments are included in the computerized clinical and management solution (French acronym: RSIPA) of Quebec’s Ministry of Health and Social Services and used by more than 15,000 professionals. Despite the advantages offered by RISPA—such as the adoption of common vocabulary used by professionals and the use of the same instruments throughout the entire service continuum—it could be improved to support professionals in making decisions when assessing and planning services as well as to take into account the values and preferences of the elderly and their families in delivering services. In response to this shortcoming, our team developed integrated care pathways (ICPs), which were designed to use the instruments mentioned above in fulfilling the needs of frail and disabled older adults and to constitute the core decision-support system providing guidance on appropriate actions for specific clinical circumstances. Before being implemented throughout Quebec, the ICPs were developed as an electronic prototype with a local home-care-service organization. The purpose of this presentation is to provide an initial picture of clinical practices prior to the implementation of the ICPs and described the efforts in deploying them.Method: Qualitative research was conducted with ISNs users. Semi-structured interviews were carried out with professionals in order to assess clinical practices that is, use of the existing OEMC instruments and the Individualized service and allocation plan as well as to determine the state of mind of professionals on the verge of the implementation. The interview guide was based on the Consolidated Framework for Implementation Research (CFIR). The interviews were recorded, transcribed, and analyzed according to CFIR themes. NVivo software was used to code and classify data.Results: A total of nine professionals from a home-care-service organization took part in the interviews, which lasted 45 minutes. Most of the interviewees were trained in social work. Their comments about RSIPA instruments were essentially positive, except in the case of the Individualized service and allocation plan. The responses indicate that the OEMC is a good instrument for service continuity. It provides a great deal of information that provides an overview and makes it possible to grasp the situation of the elderly person and his or her family. On the other hand, it is long to complete and certain information must be duplicated in other instruments. The Individualized service and allocation plan was introduced in 2014. The interviewees' perception was that this instrument responded to institutional needs and detected shortcomings in existing services rather than supporting the planning process conducted by care providers. The training on its use was deemed inadequate as was clinical and technical support. Faced with the imminent implementation of ICPs, several of the interviewees indicated that they didn't have much information about its content or implementation methods. They maintained that they had not been involved in the consultation process or even consulted about more technical issues. Moreover, a number of interviewees that they were curious and excited to find out more about the ICPs and that the ICPs could save time and yield higher-quality interventions.Discussion: It is interesting to draw relationships between interviewee criticisms related to training and support subsequent to the introduction of the Individualized service and allocation plan and what they hoped to have as training for ICP implementation. There appears to be a clear association between the lack of information and the degree of fear and apprehension. Certain interviewees stated that there was a climate of insecurity and stress in the teams and among care providers, which could be very fertile ground for the development of negative scenarios. Several care providers asked managers to approach productivity during ICP implementation with open minds and understanding. Some suggested that managers should also take the ICP training in order to better understand what it demanded of teams in terms of adaptation and learning.Conclusion: The comments made by the professionals demonstrate that the support strategy adopted when the ICPs were on the verge of being implemented could be optimized.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.030 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.014 | 0.019 |
| Scholarly communication | 0.033 | 0.021 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.010 | 0.007 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".