Healthcare Cost Analysis of Older Patients Using a Personal Emergency Response Service Uncovers Costs Savings Opportunity
Notice bibliographique
Résumé
Background: In the US healthcare system, half of overall Medicare and Medicaid Services reimbursement goes towards caring for the top 5% most expensive patients. However, little is known about how these patients' costs change annually prior to them reaching the top 5%. To address these gaps and investigate potential cost savings opportunities, we analyzed patient flow and associated healthcare cost trends over the period 2011-2015. Objective: To evaluate longitudinal trends in healthcare cost of older patients using Personal Emergency Response Service (PERS). Methods: This is a retrospective, longitudinal, multicenter study to evaluate healthcare cost of 2,643 older patients over the period 2011-2015. All patients had at least one inpatient and/or outpatient encounter, and at least one episode of home health care during the study period. In addition, all patients used PERS at home anytime during the study period. The study population was segmented by their annual healthcare expenditures into Top (5%), Middle (6-50%) and Bottom (51-100%) segments. Cost acuity pyramids were built based on these segments for each fiscal year. The longitudinal healthcare expenditure trends of the complete study population, as well as each segment, were assessed by linear regression models. Patient flows throughout the segments of the cost acuity pyramids from year to year were modeled by Markov chains. The associated costs flows were quantified over a 2-year period. Results: Total healthcare cost of the study population nearly doubled from $17.7M in 2011 to $33.0M in 2015. This increasing trend was statistically significant (P=0.003) with an expected yearly cost increase of $3.6M. This growth was driven by a significantly higher cost increase in the Middle segment ($2.3M; P=0.002). The expected yearly costs increase of the Top and Bottom segments was $1.2M (P=0.008) and $0.1M (P=0.003) respectively, and both were statistically significant. The patients and cost flow analyses showed that 18% of patients moved up the cost acuity pyramid yearly, and their cost increased by 672% in contrast to 22% of patients who moved down with a cost decrease by 86%. The remaining 60% of patients stayed in the same segment from year to year, but their cost increased by 18%. Conclusions: While many healthcare organizations target costly intensive interventions for their most expensive patients, this analysis unveiled a potential cost savings opportunity by managing the patients in the lower cost segments that are at risk of moving up the cost acuity pyramid. To achieve this, data analytics that integrate longitudinal data from the EHR and home monitoring devices may help healthcare organizations to optimize resources by enabling clinicians to proactively manage patients in their home or community environments, beyond institutional settings and 30-60 day telehealth services.
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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 | 0,001 | 0,007 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
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 ».