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1.
J Patient Saf ; 20(5): 370-374, 2024 Aug 01.
Artigo em Inglês | MEDLINE | ID: mdl-38506482

RESUMO

OBJECTIVES: Inadequate follow-up of incidental imaging findings (IIFs) can result in poor patient outcomes, patient dissatisfaction, and provider malpractice. At our institution, radiologists flag IIFs during report dictation to trigger electronic health record (EHR) notifications to providers and patients. Nurse coordinators directly contact patients or their primary care physicians (PCPs) regarding IIFs if follow-up is not completed within the recommended time frame. Despite these interventions, many patients and their PCPs remain unaware of IIFs. In an effort to improve awareness of IIFs, we aim to investigate communication of IIFs on inpatient discharge summaries after implementation of our EHR notification system. METHODS: Inpatient records with IIFs from 2018 to 2021 were retrospectively reviewed to determine type of IIFs, follow-up recommendations, and mention of IIFs on discharge summaries. Nurse coordinators spoke to patients and providers to determine their awareness of IIFs. RESULTS: Incidental imaging findings were reported in 51% of discharge summaries (711/1383). When nurse coordinators called patients and PCPs regarding IIFs at the time follow-up was due, the patients and PCPs were aware of 79% of IIFs (1096/1383). CONCLUSIONS: With implementation of EHR notifications to providers regarding IIFs, IIFs were included in 51% of discharge summaries. Lack of inclusion of IIFs on discharge summaries could be related to transitions of care within hospitalization, provider alert fatigue, and many diagnostic testing results to distill. These findings demonstrate the need to improve communication of IIFs, possibly via automating mention of IIFs on discharge summaries, and the need for care coordinators to follow up on IIFs.


Assuntos
Registros Eletrônicos de Saúde , Achados Incidentais , Humanos , Registros Eletrônicos de Saúde/estatística & dados numéricos , Estudos Retrospectivos , Comunicação , Sumários de Alta do Paciente Hospitalar , Alta do Paciente , Feminino , Masculino , Diagnóstico por Imagem/métodos , Pessoa de Meia-Idade
2.
Am J Med ; 130(3): e115, 2017 03.
Artigo em Inglês | MEDLINE | ID: mdl-28153335
3.
Am J Med ; 129(7): 699-705.e4, 2016 07.
Artigo em Inglês | MEDLINE | ID: mdl-26968469

RESUMO

BACKGROUND: Determining risk factors for opioid abuse or dependence will help clinicians practice informed prescribing and may help mitigate opioid abuse or dependence. The purpose of this study is to identify variables predicting opioid abuse or dependence. METHODS: A retrospective cohort study using de-identified integrated pharmacy and medical claims was performed between October 2009 and September 2013. Patients with at least 1 opioid prescription claim during the index period (index claim) were identified. We ascertained risk factors using data from 12 months before the index claim (pre-period) and captured abuse or dependency diagnosis using data from 12 months after the index claim (postperiod). We included continuously eligible (pre- and postperiod) commercially insured patients aged 18 years or older. We excluded patients with cancer, residence in a long-term care facility, or a previous diagnosis of opioid abuse or dependence (identified by International Classification of Diseases 9th revision code or buprenorphine/naloxone claim in the pre-period). The outcome was a diagnosis of opioid abuse (International Classification of Diseases 9th revision code 304.0x) or dependence (305.5). RESULTS: The final sample consisted of 694,851 patients. Opioid abuse or dependence was observed in 2067 patients (0.3%). Several factors predicted opioid abuse or dependence: younger age (per decade [older] odds ratio [OR], 0.68); being a chronic opioid user (OR, 4.39); history of mental illness (OR, 3.45); nonopioid substance abuse (OR, 2.82); alcohol abuse (OR, 2.37); high morphine equivalent dose per day user (OR, 1.98); tobacco use (OR, 1.80); obtaining opioids from multiple prescribers (OR, 1.71); residing in the South (OR, 1.65), West (OR, 1.49), or Midwest (OR, 1.24); using multiple pharmacies (OR, 1.59); male gender (OR, 1.43); and increased 30-day adjusted opioid prescriptions (OR, 1.05). CONCLUSIONS: Readily available demographic, clinical, behavioral, pharmacy, and geographic information can be used to predict the likelihood of opioid abuse or dependence.


Assuntos
Alcoolismo/epidemiologia , Transtornos Mentais/epidemiologia , Transtornos Relacionados ao Uso de Opioides/epidemiologia , Adulto , Fatores Etários , Estudos de Coortes , Feminino , Humanos , Modelos Logísticos , Masculino , Pessoa de Meia-Idade , Análise Multivariada , Razão de Chances , Farmácias/estatística & dados numéricos , Estudos Retrospectivos , Medição de Risco , Fatores de Risco , Fatores Sexuais , Transtornos Relacionados ao Uso de Substâncias/epidemiologia , Estados Unidos/epidemiologia
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