Return on Investment Analysis for the Integrated Parkinson’s Care Network: Lesson Learned from a Pilot Study
Bibliographic record
Abstract
BACKGROUND: Parkinson's disease (PD) is a complex and debilitating condition that requires care from a multispecialty team. The Integrated Parkinson Care Network (IPCN) is an innovative pragmatic care model that focuses on integrated care, self-management support and technology-enabled care. OBJECTIVE: This study aims to estimate the costs of the IPCN and assess whether benefits gained from the intervention offset its costs based on a single center experience. METHODS: We conducted a return on investment (ROI) analysis of the IPCN from a societal perspective. The ROI for the IPCN was estimated as a ratio of the net savings and the intervention cost. The intervention cost was calculated as a sum of set-up and implementation costs. Cost savings was measured as the absolute reduction in the societal costs realized by PD patients. A positive ROI indicated that savings generated from the intervention offset its cost. RESULTS: The total cost of the IPCN for 100 PD patients was C$135,669, or C$226 per patient per month. IPCN was associated with the reduction in societal cost of C$915 per patient per month (95%CI: -2,782, 951). The ROI per PD patient per month for the IPCN was 3.08 (95%CI: -0.60, 22.93), suggesting that for every C$1 invested in the IPCN, C$4.08 is gained through reduction in societal costs. The returns were greater among advanced PD patients. CONCLUSION: The IPCN has the potential to offer a good return on investment for PD patients, and its value for money is higher among advanced PD patients.
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 | 0.024 | 0.072 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".