Publications by authors named "Parvez A Rahman"

The Deterioration Index (DI) is an automatic early warning system that utilizes a machine learning algorithm integrated into the electronic health record and was implemented to improve risk stratification of inpatients. Our pilot implementation showed superior diagnostic accuracy than standard care. A score >60 had a specificity of 88.

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Article Synopsis
  • Threshold-based early warning systems (EWS) help predict adverse events in hospitalized patients, and machine learning (ML) algorithms using EWS scores could enhance prediction accuracy.
  • The study utilized the deterioration index (DI) to assess patient risk and developed a new ML model, achieving improved performance compared to traditional threshold models, with the top classifier significantly predicting adverse events.
  • Time-based analysis indicated the best predictive accuracy occurs within the hour before an event, but the model still performs well up to 12 hours in advance, demonstrating its reliability across multiple clinical sites.
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Objectives: To examine the prevalence, severity, and co-occurrence of SPPADE symptoms as well as their association with cancer type and patient characteristics.

Background: The SPPADE symptoms (sleep disturbance, pain, physical function impairment, anxiety, depression, and low energy /fatigue) are prevalent, co-occurring, and undertreated in oncology and other clinical populations.

Methods: Baseline SPPADE symptom data were analyzed from the E2C2 study, a stepped wedge pragmatic, population-level, cluster randomized clinical trial designed to evaluate a guideline-informed symptom management model targeting the six SPPADE symptoms.

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Objective: Hospitalized patients discharged to skilled nursing facilities (SNFs) for post-acute care are at high risk for adverse outcomes. Yet, absence of effective prognostic tools hinders optimal care planning and decision making. Our objective was to develop and validate a risk prediction model for 6-month all-cause death among hospitalized patients discharged to SNFs.

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Background: The symptom burden associated with cancer and its treatment can negatively affect patients' quality of life and survival. Symptom-focused collaborative care model (CCM) interventions can improve outcomes, but only if patients engage with them. We assessed the receptivity of severely symptomatic oncology patients to a remote nurse-led CCM intervention.

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Skilled nursing facilities (SNFs) increasingly provide care to patients after hospitalization. The Centers for Medicare & Medicaid Services reports ratings for SNFs for overall quality, staffing, health inspections, and clinical quality measures. However, the relationship between these ratings and patient outcomes remains unclear.

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Objectives: Older patients discharged to skilled nursing facilities (SNFs) for post-acute care are at high risk for hospital readmission. Yet, as in the community setting, some readmissions may be preventable with optimal transitional care. This study examined the proportion of 30-day hospital readmissions from SNFs that could be considered potentially preventable readmissions (PPRs) and evaluated the reasons for these readmissions.

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Background: The prevalence of inadequate symptom control among cancer patients is quite high despite the availability of definitive care guidelines and accurate and efficient assessment tools.

Methods: We will conduct a hybrid type 2 stepped wedge pragmatic cluster randomized clinical trial to evaluate a guideline-informed enhanced, electronic health record (EHR)-facilitated cancer symptom control (E2C2) care model. Teams of clinicians at five hospitals that care for patients with various cancers will be randomly assigned in steps to the E2C2 intervention.

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Objectives: Patients discharged to a skilled nursing facility (SNF) for post-acute care have a high risk of hospital readmission. We aimed to develop and validate a risk-prediction model to prospectively quantify the risk of 30-day hospital readmission at the time of discharge to a SNF.

Design: Retrospective cohort study.

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Background: Although posthospitalization care transitions programs (CTP) are highly diverse, their overall program thoroughness is most predictive of their success.

Objective: To identify components of a successful homebased CTP and patient characteristics that are most predictive of reduced 30-day readmissions.

Design: Retrospective cohort.

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Background: Care transition programs can potentially reduce 30 day readmission; however, the effect on long-term hospital readmissions is still unclear.

Objective: We compared short-term (30 day) and long-term (180 day) utilization of participants enrolled in care transitions versus those matched referents eligible but not enrolled.

Design: This cohort study was conducted from January 1, 2011 until June 30, 2013 within a primary care academic practice.

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Objective: Assess algorithms for linking patients across de-identified databases without compromising confidentiality.

Data Sources/study Setting: Hospital discharges from 11 Mayo Clinic hospitals during January 2008-September 2012 (assessment and validation data). Minnesota death certificates and hospital discharges from 2009 to 2012 for entire state (application data).

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Background: Approximately 20% of seniors live with five or more chronic medical illnesses. Terminal stages of their lives are often characterized by repeated burdensome hospitalizations and advance care directives are insufficiently addressed. This study reports on the preliminary results of a Palliative Care Homebound Program (PCHP) at the Mayo Clinic in Rochester, Minnesota to service these vulnerable populations.

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Background: The inclusion of mental health issues in the evaluation of multimorbidity generally has been as the presence or absence of the condition rather than severity, complexity, or stage. The hypothesis for this study was that clinical outcome of the depression 6 months after enrollment into collaborative care management would have a role in predicting future complexity of care tier.

Methods: This study was a retrospective chart review of 1894 primary care patients who were diagnosed with major depressive disorder or dysthymia as of December 2012.

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