Impact of the Artificial Intelligence (Machine Learning) in a Telemonitoring Programme of COPD Patients With Multiple Hospitalizations (telEPOC)
Trial Snapshot
- Phase
- Not Applicable
- Sponsor
- Enrollment
- 345
- Locations
- 2
- Primary Endpoint
- Change in clinical diary practice after including ML
Study Overview
Brief Summary
Given the current situation concerning healthcare, population demographics and economy, it seems required to look for new approaches in the health system. The use of new technologies must be the main factor for this change.
GENERAL OBJECTIVE:
To determine the impact that the application of an artificial intelligence system (Machine Learning) could have on an active telemonitoring programme of readmitted COPD patients.
Particular objectives: to determine the changes in:
- The use of healthcare resources.
- Patients´ quality of life.
- Costs.
- Load of work.
- Daily clinical practice.
- Inflammation markers
METHODS:
Based on the telEPOC programme and Machine Learning developement in this project, non-randomized intervention study, with two branches: intervention (Galdakao hospital) and control (Cruces and Basurto hospital).
Sample size of at least 115 patients per hospital (115 in the intervention branch and 230 in the control branch). A 2-year follow-up.
Uni and multivariate statistics will be applied.
Detailed Description
Telemonitoring programmes are an alternative to the traditional systems of patients' control, specially in chronic diseases. This kind of tools are also important because of the aging of the population, the increase in chronic diseases and the consequent increase in costs of maintenance of the health systems. On the other hand, nowadays these chronic patients are especially attended because of exacerbations, fundamentally in emergencies and hospitalization, and also in in-person scheduled consultations when patients are stable. Then, a closer attention is more desirable by the point of view of clinic, management, and costs.
COPD (Chronic Obstructive Pulmonary Disease) is a highly prevalent disease. Moreover, it has a high consumption of sanitary resources and costs, 50% of whom are due to hospitalizations.
Furthermore, exacerbations in COPD and specially the severe ones, have important consequences for patients (decrease of pulmonary function, worsening of quality of life and increase in mortality).
Because of that, telemonitoring appears to be a solution to improve the control of these patients and improve the consumption of resources. In Galdakao Hospital in Spain, it was initiated a telemonitoring programme in COPD patients who re-admit to hospital. Its primary objective was tos reduce readmissions because of COPD exacerbations and it could demonstrate a significant decrease in the use of sanitary resources (hospitalizations, visit to emergences department, readmissions and average stay days). It also demonstrated a less worsening in clinical symptoms and quality of life in more severe patients.
However, there are three factors that are very important in chronic diseases: the increase in aging people, the increase of people with chronic diseases and the fast evolution of technology, specially the recollection and information processing systems.
Study Design
- Study Type
- Interventional
- Allocation
- Non Randomized
- Intervention Model
- Parallel
- Primary Purpose
- Prevention
- Masking
- Single (Outcomes Assessor)
Masking Description
Only data entry people and biostatisticians were masking
Eligibility Criteria
- Ages
- 18 Years to 85 Years (Adult, Older Adult)
- Sex
- All
- Accepts Healthy Volunteers
- No
Inclusion Criteria
- •Having a COPD (COPD was confirmed if the post-bronchodilator forced expiratory volume in one second (FEV1) divided by the forced vital capacity (FVC) was less than 0.7 (FEV1/FVC<70%)
- •Having been admitted at least twice in the previous year or three times in the two previous years for a COPD exacerbation (eCOPD).
Exclusion Criteria
- •Another significant respiratory disease.
- •An active neoplasm.
- •A terminal clinical situation.
- •Inability to carry out any of the measurements of the project.
- •Unwillingness to take part in the study.
Outcomes
Primary Outcomes
Change in clinical diary practice after including ML
Time Frame: 2 years
- Physical activity (pedometer)
Workload of nurses
Time Frame: 2 years
- The time that must be spend every day managing the alarms after adding ML.
Changes in quality of life in patients after the implementation of ML (in patients that generate alarms)
Time Frame: 2 years
* EuroQol-5d questionnaire: measure of health for clinical and economic appraisal. 2 parts: * 5 dimensions descriptive system (mobility, self-care, usual activities, pain/discomfort, and anxiety/depression). Each of them has 3 levels of severity (no problems -1 point- , some problems -2 points- or moderate-severe problems - 3 points-). Having more points represents a worse situation. * A visual analog scale for a more general evaluation. It is a vertical scale, ranging from 0 (worst imaginable state of health) to 100 (best imaginable state of health). In it, the individual must mark the point on the vertical line that best reflects the assessment of their global health status today.
Number of resources after the implementation of ML (Machine Learning) added to a telemedicine system in readmitted COPD patients (telEPOC).
Time Frame: 2 years
* Number of hospitalizations (hospital base data). * Days of hospital staying ((hospital base data). * Emergency visits (hospital base data). * Readmissions (hospital base data). * Visits to pneumology consultation in last 2 years (hospital base data).
Change in quality of life in patients after the implementation of ML (in patients that generate alarms)
Time Frame: 2 years
-CAT (COPD assessment test): impact of COPD on health status. 8 items (cough, phlegm, chest tightness, breathlessness, limited activities, confidence leaving home, sleeplessness and energy), scaling from 1 to 5. Higher scores denote a more severe impact of COPD on a patient's life.
Cost of the implementation of ML in relation to the standard telemonitoring programmes
Time Frame: 2 years
- Economic evaluation, inlcuding all the interventions carried out inherent to the program, ranging from phone calls, patient displacement for consultation, drug use, hospitalizations and visits to emergencioes, primary and specialized care (hospital base data).
Changes in clinical diary practice after including ML
Time Frame: 2 years
- Exercise capacity (six minutes walking test)
Secondary Outcomes
No secondary outcomes reported
Investigators
Dr. Cristobal Esteban
MD
Hospital Galdakao-Usansolo