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6 citation(s) found for "Goldstein, Richard".

Community-based, Hospital-Affiliated Medical Group Becomes a Successful Accountable Care Organization
Madden, Kathleen B., DoRosario, Arnold, O’Connell, Ryan M. , Aberizk, William, Brown, Karen , Guttman, Carrie, Goldstein, Richard , Logue, Peter, Varkey, Prathibha, Madden, Kathleen, Madden, Kathleen, O’Connell, Ryan

Mayo Clinic

American Journal of Medical Quality | October 10, 2022 | Volume: 37 | Issue: 6 | Pages: 511-518

10.1097/jmq.0000000000000085 | PMID 36214586 Get It

Leadership of a multispecialty group practice within a health system recognized in 2015 that population health management requires quality performance improvement and organizational culture change. While blueprints for building successful Accountable Care Organizations (ACOs) exist in the literature, few describe the journey to achieving both shared savings and high quality outcomes achieved by a medical group within an academic health system. Clinician education and engagement, prioritizing prevention and achieving benchmarks, developing supportive roles, more precise documentation of accurate diagnostic coding, and risk stratification constituted the approach. When first participating as an ACO, the medical group built programs and teams to improve quality, while CMS simultaneously changed quality measurements from pay-for-reporting to pay-for-performance. Quality scores initially dipped, though scores have since risen to 98.44% in 2020. Between 2015 and 2017, financial results were more than $10 million below the threshold, while in performance years 2018 to 2020, Northeast Medical Group achieved $24 million in aggregate in shared savings.
Gender Differences in Time Spent on Documentation and the Electronic Health Record in a Large Ambulatory Network
Fong, Allan S., Jeffery, Molly M. , Sinsky, Christine A. , Goldstein, Richard , Williams, Brian , Melnick, Edward R. , Jeffery, Molly Moore , Melnick, Edward R , Rotenstein, Lisa S.

Mayo Clinic

JAMA Network Open | March 24, 2022 | Volume: 5 | Issue: 3 | Pages: e223935

10.1001/jamanetworkopen.2022.3935 | PMID 35323954 Get It

How a Virtual Scribe Program Improves Physicians’ EHR Experience, Documentation Time, and Note Quality
Ong, S. , Williams, Brian , O’Connell, Ryan , Goldstein, Richard , Melnick, Edward R. , Melnick, Edward R , Jeffery, Molly Moore

Mayo Clinic

NEJM Catalyst | November 17, 2021 | Volume: 2 | Issue: 12 | Pages: No page available

10.1056/cat.21.0294 Get It

Trends in Electronic Health Record Inbox Messaging During the COVID-19 Pandemic in an Ambulatory Practice Network in New England
Nath, Bidisha , Williams, Brian , Jeffery, Molly M. , O’Connell, Ryan , Goldstein, Richard , Sinsky, Christine A. , Melnick, Edward R. , Jeffery, Molly Moore , Melnick, Edward R

Mayo Clinic

JAMA Network Open | October 13, 2021 | Volume: 4 | Issue: 10 | Pages: e2131490

10.1001/jamanetworkopen.2021.31490 | PMID 34636917 Get It

Developing an Index of Medical Conditions Associated with Outcomes after Moderate-to-Severe Traumatic Brain Injury
Whiteneck, Gale G. , Ketchum, Jessica M. , Almeida, Emily, Goldstein, Richard , Hammond, Flora M., Weintraub, Alan, Tefertiller, Candice, Brown, Allen W. , Corrigan, John D.

Mayo Clinic

Journal of Neurotrauma | December 1, 2020 | Volume: 38 | Issue: 5 | Pages: 593-603

10.1089/neu.2020.7366 | PMID 33256501 Get It

Medical conditions co-occurring with traumatic brain injury (TBI) are associated with outcomes, and comorbidity indices such as Charlson and Elixhauser are used in TBI research, but they are not TBI specific. The purpose of this research was to develop an index or indices of medical conditions, identified in acute care after moderate to severe TBI, that are associated with outcomes at rehabilitation discharge. Using the TBI Model Systems National Database, the International Classification of Diseases, Ninth Revision (ICD-9) and Tenth Revision (ICD-10) codes of 8988 participants were converted to Healthcare Cost and Utilization Project (HCUP) diagnostic categories. Poisson regression models were built predicting Disability Rating Scale and Functional Independence Measure Cognitive and Motor subscale scores from HCUP categories after controlling for demographic and injury characteristics. Unweighted, weighted, and anchored indices based on the outcome models predicted 7.5–14.3% of the variance in the observed outcomes. When the indices were applied to a new validation sample of 1613 cases, however, only 2.6–6.6% of the observed outcomes were predicted. Therefore, no models or indices were recommended for future use, but several study findings are highlighted suggesting the importance and the potential for future research in this area.
Predicting Institutionalization after Traumatic Brain Injury Inpatient Rehabilitation
Eum, Regina S., Seel, Ronald T. , Goldstein, Richard , Brown, Allen W. , Watanabe, Thomas K. , Zasler, Nathan D. , Zafonte, Ross , Glenn, Mel B., Roth, Elliot J.

Mayo Clinic

Journal of Neurotrauma | September 15, 2014 | Volume: 32 | Issue: 4 | Pages: 280-286

10.1089/neu.2014.3351 | PMID 25203001 Get It

Risk factors contributing to institutionalization after inpatient rehabilitation for people with traumatic brain injury (TBI) have not been well studied and need to be better understood to guide clinicians during rehabilitation. We aimed to develop a prognostic model that could be used at admission to inpatient rehabilitation facilities to predict discharge disposition. The model could be used to provide the interdisciplinary team with information regarding aspects of patients' functioning and/or their living situation that need particular attention during inpatient rehabilitation if institutionalization is to be avoided. The study population included 7219 patients with moderate-severe TBI in the Traumatic Brain Injury Model Systems (TBIMS) National Database enrolled from 2002–2012 who had not been institutionalized prior to injury. Based on institutionalization predictors in other populations, we hypothesized that among people who had lived at a private residence prior to injury, greater dependence in locomotion, bed-chair-wheelchair transfers, bladder and bowel continence, feeding, and comprehension at admission to inpatient rehabilitation programs would predict institutionalization at discharge. Logistic regression was used, with adjustment for demographic factors, proxy measures for TBI severity, and acute-care length-of-stay. C-statistic and predictiveness curves validated a five-variable model. Higher levels of independence in bladder management (adjusted odds ratio [OR], 0.88; 95% CI 0.83, 0.93), bed-chair-wheelchair transfers (OR, 0.81 [95% CI, 0.83–0.93]), and comprehension (OR, 0.78 [95% CI, 0.68, 0.89]) at admission were associated with lower risks of institutionalization on discharge. For every 10-year increment in age was associated with a 1.38 times higher risk for institutionalization (95% CI, 1.29, 1.48) and living alone was associated with a 2.34 times higher risk (95% CI, 1.86, 2.94). The c-statistic was 0.780. We conclude that this simple model can predict risk of institutionalization after inpatient rehabilitation for patients with TBI.