Utilization of Prostate Cancer Quality Metrics for Research and Quality Improvement: A Structured Review

Gori, Davide; Dulal, Rajendra; Blayney, Douglas W.; Brooks, James D.; Fantini, Maria Pia; McDonald, Kathryn M; Hernandez‐Boussard, Tina

doi:10.1016/j.jcjq.2018.06.004

Cited by 8 publications

(9 citation statements)

References 53 publications

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“…Together, these studies suggest that early functional recovery improves hospital outcomes and decreases health care utilization. More important, as quality measures expand to encompass more granular outcomes and facets of patient experience across episodes of care, physical function demonstrates value as an outcome measure with meaningful patient benefit that translates across health care settings because improved physical function has been associated with a faster return to community participation, decreased patient costs, and less time spent in the hospital.…”

Section: Discussionmentioning

confidence: 99%

Utility of Functional Metrics Assessed During Acute Care on Hospital Outcomes: A Systematic Review

Lage

Slocum

et al. 2019

PM&R

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Objective: Traditionally, illness severity, social factors, and comorbid conditions have been examined as predictors of hospital outcomes. However, recent research in the rehabilitation setting demonstrated that physical function outperformed comorbidity indices as a predictor of 30-day readmission. The purpose of this study was to review the literature examining the association between acute hospital physical function and various hospital outcomes and health care utilization. Type: Systematic review. Literature Survey: A review of the MEDLINE database was performed. Search terms included acute functional outcomes and frailty outcomes. Studies up to September 2017 were included if they were in English and examined how functional metrics collected at acute care hospitalization affected hospital outcomes. Methodology: Cohort characteristics and measures of associations were extracted from the studies. Outcomes include hospital readmission, length of stay, mortality, discharge location, and physical function post acute care. The studies were assessed for potential confounders as well as selection, attrition, and detection bias. Synthesis: A total of 30 studies were identified (hospital readmissions: 6; discharge location: 11; length of stay: 4; mortality: 15; function: 6). Thirteen different metrics assessed function during acute care. Lower function during acute care was associated with statistically significant higher odds of hospital readmission, lower likelihood of discharge to home, longer hospital length of stay, increased mortality, and worse functional recovery when compared to patients with higher function during acute care, when adjusted for age and gender. The Barthel Index may be a useful marker for mortality in the elderly whereas the Functional Independence Measure instrument may be valuable for examining discharge location. Conclusions: There is increasing evidence that function measured during acute care predicts a broad array of meaningful clinical outcomes. Further research would help direct the use of practical, yet parsimonious functional metrics that effectively screen high-need, high-cost patients to deliver optimal care.

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Section: Discussionmentioning

confidence: 99%

Utility of Functional Metrics Assessed During Acute Care on Hospital Outcomes: A Systematic Review

Lage

Slocum

et al. 2019

PM&R

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“…On the other hand, many of the >100 quality metrics proposed in prostate cancer are based on expert opinion and other low-level evidence 8. Given the widespread concerns about ‘click fatigue’ and physician burn-out related to documentation in the EHR (PMID: 29801050), required structured fields need to be deployed wisely and judiciously.…”

Section: Discussionmentioning

confidence: 99%

“…Performance of a digital rectal exam has been identified as an important quality metric in prostate cancer care,8 9 and most clinical guidelines include DRE as part of a comprehensive pretreatment assessment 6 7. However, DRE results are often not systematically recorded nor included in the billing or claims datasets used in outcomes research.…”

Section: Introductionmentioning

confidence: 99%

Is it possible to automatically assess pretreatment digital rectal examination documentation using natural language processing? A single-centre retrospective study

et al. 2019

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ObjectivesTo develop and test a method for automatic assessment of a quality metric, provider-documented pretreatment digital rectal examination (DRE), using the outputs of a natural language processing (NLP) framework.SettingAn electronic health records (EHR)-based prostate cancer data warehouse was used to identify patients and associated clinical notes from 1 January 2005 to 31 December 2017. Using a previously developed natural language processing pipeline, we classified DRE assessment as documented (currently or historically performed), deferred (or suggested as a future examination) and refused.Primary and secondary outcome measuresWe investigated the quality metric performance, documentation 6 months before treatment and identified patient and clinical factors associated with metric performance.ResultsThe cohort included 7215 patients with prostate cancer and 426 227 unique clinical notes associated with pretreatment encounters. DREs of 5958 (82.6%) patients were documented and 1257 (17.4%) of patients did not have a DRE documented in the EHR. A total of 3742 (51.9%) patient DREs were documented within 6 months prior to treatment, meeting the quality metric. Patients with private insurance had a higher rate of DRE 6 months prior to starting treatment as compared with Medicaid-based or Medicare-based payors (77.3%vs69.5%, p=0.001). Patients undergoing chemotherapy, radiation therapy or surgery as the first line of treatment were more likely to have a documented DRE 6 months prior to treatment.ConclusionEHRs contain valuable unstructured information and with NLP, it is feasible to accurately and efficiently identify quality metrics with current documentation clinician workflow.

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“…This pipeline can be implemented at point of care, such as by providing reminders to providers to perform a test like a bone scan. In addition, by gathering and presenting all information necessary to guide bone scan decisions, the methods can be used to assess the need for and performance of guidelines (such as those based solely on expert opinion) in special 36 DRE performance is identified as an important quality metric in prostate cancer care, 10,37 and most clinical guidelines include DRE as part of a comprehensive pretreatment assessment. 38,39 However, DRE results are often not systematically recorded nor included in claims datasets.…”

Section: Applications Of Captive In Assessing Qualitymentioning

confidence: 99%

“…Claims data generally include encounter-level information regarding diagnoses, treatments and billing information yet are limited in their capture of patient outcomes (particularly patient-reported outcomes), clinical decisions, patient-values, and secondary diagnoses/problem lists. 8,9 These data limitations resulted in an abundance of quality measures focused on processes of care 10 that have been subsequently linked to clinician burnout 11 and also too few measures that are actually linked to improvements in patient outcomes, which are of importance to patients and their caregivers, as well as health care purchasers. 12 Advances in informatics and digitalization of information further promised a positive transformation of health care.…”

Section: Introductionmentioning

confidence: 99%

Leveraging Digital Data to Inform and Improve Quality Cancer Care

Hernandez‐Boussard

Blayney

Brooks

2020

Cancer Epidemiology, Biomarkers &Amp; Prevention

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Background: Efficient capture of routine clinical care and patient outcomes are needed at a population-level, as is evidence on important treatment-related side effects and their effect on well-being and clinical outcomes. The increasing availability of electronic health records (EHRs) offers new opportunities to generate population-level patient-centered evidence on oncological care that can better guide treatment decisions and patient-valued care.Methods: This study includes patients seeking care at an academic medical center, 2008-2018. Digital data sources are combined to address missingness, inaccuracy, and noise common to EHR data. Clinical concepts were identified and extracted from EHR unstructured data using natural language processing (NLP) and machine/deep learning techniques. All models are trained, tested, and validated on independent data samples using standard metrics. Results:We provide use cases for using EHR data to assess guideline adherence and quality measurements among cancer patients. Pretreatment assessment was evaluated by guideline adherence and quality metrics for cancer staging metrics. Our studies in perioperative quality focused on medications administered and guideline adherence.Patient outcomes included treatment-related side-effects and patient-reported outcomes.Conclusions: Advanced technologies applied to EHRs present opportunities to advance population-level quality assessment, to learn from routinely collected clinical data for personalized treatment guidelines, and to augment epidemiological and

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Utilization of Prostate Cancer Quality Metrics for Research and Quality Improvement: A Structured Review

Cited by 8 publications

References 53 publications

Utility of Functional Metrics Assessed During Acute Care on Hospital Outcomes: A Systematic Review

Utility of Functional Metrics Assessed During Acute Care on Hospital Outcomes: A Systematic Review

Is it possible to automatically assess pretreatment digital rectal examination documentation using natural language processing? A single-centre retrospective study

Leveraging Digital Data to Inform and Improve Quality Cancer Care

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