Examining the Usefulness of Patient Documentation Forms as a Tool for Community Health Navigators
Bibliographic record
Abstract
Introduction | Effective documentation of patient encounters may influence Community Health Navigators’ (CHNs) success in providing support to patients as well as provide a data source to examine CHN practices. The ENhancing COMmunity health through Patient navigation, Advocacy, and Social Support (ENCOMPASS) study, based in partnership between the University of Calgary and the Mosaic Primary Care Network (MPCN) is evaluating a CHN program to determine whether CHNs improve outcomes for patients with multiple chronic conditions. CHNs support their patients by helping them navigate the health system, connect to community resources, and access culturally appropriate support. The purpose of this study was to examine the quality and usefulness of CHN-patient documentation forms used in the ENCOMPASS pilot study (i.e., Initial Action Planning Form, Follow-up Action Planning Form, Patient Encounter Form, all implemented on the REDCap platform) and revise the documentation process using co-design with the end user.
 Methods | An iterative co-design quality improvement process was employed across three phases. First, content analyses were conducted on the Patient Encounter Form notes to examine how CHNs were using the forms and how they were documenting their activities. Second, a survey was distributed to CHNs to gather their perspectives about their experiences with the REDCap platform and the three forms. Third, a working group, consisting of four CHNs, met twice with research team members to discuss barriers to use and opportunities for improvement.
 Results | The REDCap platform and the three CHN-patient encounter forms did not adequately meet the needs of the CHNs. Content analysis revealed significant variation in how the Patient Encounter Form was utilized and various form sections were not completed as intended. In the survey, CHNs reported that the documentation experience was not satisfactory and the training that they had received to date was insufficient. The CHN working group suggested changes to the interface with the REDCap platform and form structure. Revisions were made based on these suggestions, and approved by the working group.
 Conclusions | The approved changes to REDCap and the three forms will be implemented and introduced to the CHN team. The research team will develop a patient encounter documentation guidelines document and will provide all members of the CHN team with the opportunity to receive re-training. These changes will be reviewed with the CHNs to continue the iterative quality improvement process. Prior to final implementation, consultation with the Clinical Research Unit administrators on the feasibility of the revisions made to the forms and interface with the REDCap platform will be held. The results of this study have the potential to provide a better overall experience for CHNs in the ENCOMPASS program and enhance their work with patients.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".