Trends in health services utilization, medication use, and health conditions among older adults: a 2-year retrospective chart review in a primary care practice
Bibliographic record
Abstract
BACKGROUND: Population aging poses significant challenges to primary care providers and healthcare policy makers. Primary care reform can alleviate the pressures, but these initiatives require clinical benchmarks and evidence regarding utilization patterns. The objectives of this study is to measure older patients' use of health services, number of health conditions, and use of medications at the level of a primary care practice, and to investigate age- and gender-related utilization trends. METHODS: A cross-sectional chart audit over a 2-year study period was conducted in the academic family practice clinic of Sunnybrook Health Sciences Centre in Toronto, Ontario, Canada. All patients 65 years and older (n = 2450) were included. Main outcome measures included the number of family physician visits, specialist visits, emergency room visits, surgical admissions, diagnostic test days, inpatient hospital admissions, health conditions, and medications. RESULTS: Older patients (80-84 and 85+ age-group) had significantly more family physician visits (average of 4.4 visits per person per year), emergency room visits (average of 0.22 ER visits per year per patient), diagnostic days (average of 5.1 test days per person per year), health conditions (average of 7.7 per patient), and medications average of 8.2 medications per person). Gender differences were also observed: females had significantly more family physician visits and number of medications, while men had more specialist visits, emergency room visits, and surgical admissions. There were no gender differences for inpatient hospital admissions and number of health conditions. With the exception of the 85+ age group, we found greater intra-group variability with advancing age. CONCLUSION: The data present a map of greater interaction with and dependency on the health care system with advancing age. The magnitudes are substantial and indicate high demands on patients and families, on professional health care providers, and on the health care system itself. There is the need to create and evaluate innovative models of care of multiple chronic conditions in the late life course.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".