Personal Health Record System Assists Men with Prostate Cancer with Access to Their Electronic Medical Records and eHealth Tools
Bibliographic record
Abstract
Topic Areas: New models for healthcare delivery, next generation electronic health records. Hypotheses: Personal Health Records (PHR) and the Internet are effective means of providing prostate cancer (PC) patients access to their electronic health records (EHR), health information and e-health tools in a secure and private manner and will assist them to meet their health information needs prior to, during and following prostate cancer treatment. Objectives: Develop and test a PHR accessible over the Internet for PC patients (called PROVIDER). Determine the usage pattern and patient satisfaction with PROVIDER. Background: PC is a disease that fits the chronic disease model in many respects where there can be a role for self-care and self-management. Cancer patients can be highly information seeking and desire access to their medical records. Providing access to medical records in the form of electronic health records (EHR) through the use of Personal Health Records (PHR) and Internet is an innovative means to meet this need in the 21st century. Methods: This was a qualitative, exploratory-type research study. After informed written consent, 22 men with a diagnosis of PC registered at the BC Cancer Agency (BCCA) in Victoria, British Columbia, Canada were given secure and private access to PROVIDER where they could access their up-to-date EHR and e-health tools. E-health tools consisted of decision support, educational, laboratory test monitoring aids and other features. Study patients were given a tutorial prior to PROVIDER use. Patients were given access to PROVIDER for 6 months. They were asked to keep a diary or log of all communications and correspondence with healthcare providers at BCCA and were interviewed at end of 6 months to share their opinions on usability, satisfaction, concerns with PROVIDER. Website activity was electronically recorded to assess usage patterns. Results: Median age of study patients was 64 years. Men were in the following phases of care when they were enrolled in the study: 19 percent initial diagnosis/work-up, 43 percent active treatment, 10 percent follow-up, 29 percent cancer recurrence. The mean number of logins per month was 3.4. Usage was most frequent during the first two months of access but was maintained at a lower rate throughout remainder of the 6 months of access. Seventy seven percent felt their privacy and security was preserved. Twenty nine percent encountered some minor difficulties using PROVIDER. The two most commonly accessed EHR were laboratory tests results and transcribed doctor notes. Ninety-four percent were satisfied to very satisfied with access to their EHR. Sixty-five percent of men said that PHR helped answer all their questions. Seventy-seven percent felt their privacy and confidentiality were preserved. Sixty-five percent felt that using PROVIDER helped them communicate better with their physicians. Eighty-three percent of patients found new and useful information using PROVIDER that they would not have received by talking to their healthcare providers. Discussion: This study demonstrates that men in various phases of care were very satisfied with PHR (i.e. PROVIDER) and would continue to use PROVIDER if it were available. The results of this study strongly suggests that PHR may assist cancer patients with timely access to their health information and medical records, and assist with communication with healthcare providers, knowledge generation, thus empowering patients to take a more active role in their own care.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".