A75 AUTOMATED FOLLOW-UP USING A PATIENT-GUIDED COMPLICATION TRACKING SYSTEM (PACTS): AN UPDATE ON PROGRESS
Notice bibliographique
Résumé
Abstract Background In recent years, there has been an increase in automated interventions in medicine. The COVID-19 outbreak has further fueled this rise. In response to the pandemic, Healthcare systems have developed a multitude of technological strategies for case identification and contact tracing. It is in this evolving digital landscape, that a PAtient-guided Complication Tracking System (PACTS) was launched. PACTS allows clinics to track complications using the Short Message Service (SMS). This program also offers opportunities to augment medical services and support patients having complications. Before PACTS can be widely implemented in clinics, research needs to be conducted to investigate its potential as a complication tracking software. Aims To assess the outcomes of an automated follow-up program implemented at St. Paul’s Hospital in Vancouver, BC. Methods A prospective study was designed to contact outpatients one-week post-procedure using PACTS. This program was delivered in two phases. Stage 1 ran from November 2019-March 2020. During this pilot stage, patients having a colonoscopy or gastroscopy were asked to participate in the study. Stage 2 ran from August 2020-August 2021. For this phase, patients having a colonoscopy, gastroscopy or flexible sigmoidoscopy were automatically enrolled in the study. An independent t-test was completed to assess response rate differences between stages. SMS responses were recorded and patients having unplanned events were contacted by phone to categorize complications. Adverse events (AE) were defined as side-effects requiring telehealth follow-up or emergency room visitation. Severe adverse events (SAE) were classified as complications requiring admission to hospital (>24 hrs). Results SMS prompts were sent to 6975 patients and the overall mean response rate was 89%. The mean response rates from Stages 1 and 2 were 92% and 88% respectively. The independent t-test revealed a statistically significant difference in response rates between phases, two-sample t(174) = 4.56, p = 9.58 x 10–6. 498 (8%) of SMS respondents reported having unplanned events. Of these patients, 372 (75%) were reached by phone and 257 (69%) were confirmed to have had a side effect. 65 of these complications were AEs and of these, 3 cases were SAEs. The most common AEs were abdominal pain (37%), bleeding (35%), nausea and vomiting (14%). Conclusions The high response rates achieved during this study provide further evidence for the use of automated follow-up systems in medicine. This study also demonstrates the potential of PACTS as a complication tracking software. Future research should devise strategies to optimize the collection of complication data using an SMS-based service. Funding Agencies None, NRC
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,021 | 0,024 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,004 | 0,004 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,005 | 0,003 |
| Science ouverte | 0,003 | 0,002 |
| Intégrité de la recherche | 0,002 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,002 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».