scholarly journals The GeriPACT Initiative to Prevent All-Cause 30-Day Readmission in High Risk Elderly

Geriatrics ◽  
2021 ◽  
Vol 6 (1) ◽  
pp. 4
Author(s):  
James S. Powers ◽  
Lovely Abraham ◽  
Ralph Parker ◽  
Nkechi Azubike ◽  
Ralf Habermann

Background: Suboptimal care transitions increases the risk of adverse events resulting from poor care coordination among providers and healthcare facilities. The National Transition of Care Coalition recommends shifting the discharge paradigm from discharge from the hospital, to transfer with continuous management. The patient centered medical home is a promising model, which improves care coordination and may reduce hospital readmissions. Methods: This is a quality improvement report, the geriatric patient-aligned care team (GeriPACT) at Tennessee Valley Healthcare System (TVHS) participated in ongoing quality improvement (Plan, Do, Study, Act (PDSA)) cycles during teamlet meetings. Post home discharge follow-up for GeriPACT patients was provided by proactive telehealth communication by the Registered Nurse (RN) care manager and nurse practitioner. Periodic operations data obtained from the Data and Statistical Services (DSS) coordinator informed the PDSA cycles and teamlet meetings. Results: at baseline (July 2018–June 2019) the 30-day all-cause readmission for GeriPACT was 21%. From July to December 2019, 30-day all-cause readmissions were 13%. From January to June 2020, 30-day all-cause readmissions were 15%. Conclusion: PDSA cycles with sharing of operations data during GeriPACT teamlet meetings and fostering a shared responsibility for managing high-risk patients contributes to improved outcomes in 30-day all-cause readmissions.

Blood ◽  
2020 ◽  
Vol 136 (Supplement 1) ◽  
pp. 32-33
Author(s):  
Srdan Verstovsek ◽  
Anne Jacobson ◽  
Jeffrey D Carter ◽  
Tamar Sapir

Background Care coordination can be especially challenging in the setting of rare malignancies such as myelofibrosis (MF), where hematology/oncology teams have limited experience working together to implement rapidly evolving standards of care. In this quality improvement (QI) initiative, we assessed barriers to patient-centered MF care in 3 community oncology systems and conducted team-based audit-feedback (AF) sessions within each system to facilitate improved care coordination. Methods Between 1/2020 and 3/2020, 31 hematology/oncology healthcare professionals (HCPs) completed surveys designed to characterize self-reported practice patterns, challenges, and barriers to collaborative MF care in 3 community oncology systems (Table 1). Building on findings from the team-based surveys, 39 HCPs from these centers participated in AF sessions to reflect on their own practice patterns and to prioritize areas for improved MF care delivery. Participants developed team-based action plans to overcome identified challenges, including barriers to effective risk stratification, care coordination, and shared decision-making (SDM) for patients with MF. Surveys conducted before and after the small-group AF sessions evaluated changes in participants' beliefs and confidence in delivering collaborative, patient-centered MF care. Results Team-Based Surveys: HCPs identified managing MF-associated anemia and other disease symptoms (42%), providing individualized care despite highly variable clinical presentations (29%), and developing institutional expertise despite low patient numbers (16%) as the most pressing challenges in MF care. For patients who are candidates for JAK inhibitor therapy, HCPs reported most commonly relying on current guidelines (71%) and clinical evidence (61%) to guide treatment selection. HCPs also considered drug safety/tolerability profiles (55%), personal or institutional experience (13%), and out-of-pocket costs for patients (13%); no participants (0%) reported incorporating patient preference into their decision-making. Teams were underutilizing SDM and patient-centered care resources; fewer than 50% reported providing tools to support adherence (48%), visual aids for patient education (47%), financial toxicity counseling (40%), resources for managing MF-related fatigue (36%), or counseling to reduce risk factors for CVD, bleeding, and thrombosis (26%). Small-Group AF Sessions: Across the 3 oncology centers, teams participating in the AF sessions (Table 1) shared a self-reported caseload of 97 patients with MF per month. HCPs reported a meaningful shift in beliefs regarding the importance of collaborative care: following the AF sessions, 100% of HCPs agreed or strongly agreed that collaboration across the extended oncology care team is essential for achieving MF treatment goals, an increase from 71% prior to the AF sessions (Figure 1). Participants also reported increased confidence in their ability to perform each of 6 aspects of evidence-based, collaborative, patient-centered care (Figure 2). In selecting which aspects of patient-centered care to address with their clinical teams, HCPs most commonly prioritized individualizing treatment decision-making based on patient- and disease-related factors (57%), followed by providing adequate patient education about treatment options and potential side effects (24%) and engaging patients in SDM (18%). To achieve these goals, 73% of HCPs committed to sharing their action plans with additional clinical team members; others committed to creating a quality task force to oversee action-plan implementation (15%) and securing buy-in from leadership and stakeholders (9%). Conclusions As a result of participating in this community-based QI initiative, hematology/oncology HCPs demonstrated increased confidence in their ability to deliver patient-centered MF care and improved commitment to team-based collaboration. Remaining practice gaps and challenges can inform future QI programs. Study Sponsor Statement The study reported in this abstract was funded by an independent educational grant from Incyte Corporation. The grantors had no role in the study design, execution, analysis, or reporting. Disclosures Verstovsek: ItalPharma: Research Funding; CTI Biopharma Corp: Research Funding; Promedior: Research Funding; Gilead: Research Funding; NS Pharma: Research Funding; Celgene: Consultancy, Research Funding; Novartis: Consultancy, Research Funding; Genentech: Research Funding; Sierra Oncology: Consultancy, Research Funding; PharmaEssentia: Research Funding; AstraZeneca: Research Funding; Incyte Corporation: Consultancy, Research Funding; Blueprint Medicines Corp: Research Funding; Protagonist Therapeutics: Research Funding; Roche: Research Funding.


2020 ◽  
Vol 73 ◽  
pp. S690-S691
Author(s):  
Angela Liaros ◽  
Christine Connolly ◽  
Lucy Potter ◽  
Lisa Jones ◽  
Tamsin Gledhill ◽  
...  

2020 ◽  
Vol 11 (04) ◽  
pp. 570-577
Author(s):  
Santiago Romero-Brufau ◽  
Kirk D. Wyatt ◽  
Patricia Boyum ◽  
Mindy Mickelson ◽  
Matthew Moore ◽  
...  

Abstract Background Hospital readmissions are a key quality metric, which has been tied to reimbursement. One strategy to reduce readmissions is to direct resources to patients at the highest risk of readmission. This strategy necessitates a robust predictive model coupled with effective, patient-centered interventions. Objective The aim of this study was to reduce unplanned hospital readmissions through the use of artificial intelligence-based clinical decision support. Methods A commercially vended artificial intelligence tool was implemented at a regional hospital in La Crosse, Wisconsin between November 2018 and April 2019. The tool assessed all patients admitted to general care units for risk of readmission and generated recommendations for interventions intended to decrease readmission risk. Similar hospitals were used as controls. Change in readmission rate was assessed by comparing the 6-month intervention period to the same months of the previous calendar year in exposure and control hospitals. Results Among 2,460 hospitalizations assessed using the tool, 611 were designated by the tool as high risk. Sensitivity and specificity for risk assignment were 65% and 89%, respectively. Over 6 months following implementation, readmission rates decreased from 11.4% during the comparison period to 8.1% (p < 0.001). After accounting for the 0.5% decrease in readmission rates (from 9.3 to 8.8%) at control hospitals, the relative reduction in readmission rate was 25% (p < 0.001). Among patients designated as high risk, the number needed to treat to avoid one readmission was 11. Conclusion We observed a decrease in hospital readmission after implementing artificial intelligence-based clinical decision support. Our experience suggests that use of artificial intelligence to identify patients at the highest risk for readmission can reduce quality gaps when coupled with patient-centered interventions.


2020 ◽  
Vol 68 (6) ◽  
pp. 1307-1312
Author(s):  
Joseph G. Ouslander ◽  
Bernardo Reyes ◽  
Sanya Diaz ◽  
Gabriella Engstrom

2020 ◽  
Author(s):  
Jane Brock ◽  
Brianna Gass ◽  
Alaina Brothersen ◽  
Lacey McFall ◽  
Kati Walsh ◽  
...  

Abstract Background Quality Improvement Networks Quality Improvement Organizations (QIN-QIOs) developed community coalitions to align care coordination efforts for Medicare beneficiaries in order to reduce readmission rates within geographically defined communities. This CMS (Centers for Medicare & Medicaid Services) funded national quality improvement program worked with 380 coalitions from 2014-2019, facilitating a variety of interventions within each community. Baseline readmission rates among communities, calculated from claims data, varied from 17.7 to 112 readmissions/1000 beneficiaries. Program results ranged from +40.7% (high performance) to -35.8% (low performance) relative improvement.We applied an implementation framework (CFIR) to the QIN-QIO efforts to define common characteristics of interventions, implementation strategies, and contexts in which improvement efforts took place. We identify features associated with successful and unsuccessful intervention implementation, and with changes in readmission rates.Methods We selected 22 communities representing a range of relative improvement, geographic characteristics and baseline readmissions rates. We measured the QIN-QIO’s perception of influence of individual CFIR constructs on community readmission rates over time using a written assessment and elicited details and mechanisms through structured interviews. Two independent reviewers qualitatively coded transcribed interviews. Final ratings for the influence of each CFIR construct on community performance were assigned by consensus, ranging from -2 (strong negative influence) to +2 (strong positive influence).Results Some adaptation of the CFIR, such including codes in a coalition domain, and adding constructs to the outer setting domain, such as healthcare market characteristics, helped fit the framework to the QIN-QIO work. The characteristics of individuals domain was less applicable to this study. Several constructs were found to be associated with improvement, or lack of, in readmission rates in communities.Conclusions The CFIR is an appropriate taxonomy for understanding implementation of care coordination interventions in the QIN-QIO communities, with constructs from the Outer Setting and Process domains having the most influence on successful implementation. Communities effectively reducing readmissions had coalitions with favorable implementation climates, robust stakeholder engagement strategies, and interventions aligned with local concerns and capabilities. The CFIR can help guide, monitor and evaluate community-based improvement initiatives, although further development some constructs is needed.


2020 ◽  
Author(s):  
Jane Brock ◽  
Brianna Gass ◽  
Alaina Brothersen ◽  
Lacey McFall ◽  
Kati Walsh ◽  
...  

Abstract BackgroundQuality Improvement Networks Quality Improvement Organizations (QIN-QIOs) developed community coalitions to align care coordination efforts for Medicare beneficiaries in order to reduce readmission rates within geographically defined communities. This CMS (Centers for Medicare & Medicaid Services) funded national quality improvement program worked with 380 coalitions from 2014-2019, facilitating a variety of interventions within each community. Baseline readmission rates among communities, calculated from claims data, varied from 17.7 to 112 readmissions/1000 beneficiaries. Program results ranged from +40.7% (high performance) to -35.8% (low performance) relative improvement.We applied an implementation framework (CFIR) to the QIN-QIO efforts to define common characteristics of interventions, implementation strategies, and contexts in which improvement efforts took place. We identify features associated with successful and unsuccessful intervention implementation, and with changes in readmission rates.MethodsWe selected 22 communities representing a range of relative improvement, geographic characteristics and baseline readmissions rates. We measured the QIN-QIO’s perception of influence of individual CFIR constructs on community readmission rates over time using a written assessment and elicited details and mechanisms through structured interviews. Two independent reviewers qualitatively coded transcribed interviews. Final ratings for the influence of each CFIR construct on community performance were assigned by consensus, ranging from -2 (strong negative influence) to +2 (strong positive influence).Results Some adaptation of the CFIR, such including codes in a coalition domain, and adding constructs to the outer setting domain, such as healthcare market characteristics, helped fit the framework to the QIN-QIO work. The characteristics of individuals domain was less applicable to this study. Several constructs were found to be associated with improvement, or lack of, in readmission rates in communities.ConclusionsThe CFIR is an appropriate taxonomy for understanding implementation of care coordination interventions in the QIN-QIO communities, with constructs from the Outer Setting and Process domains having the most influence on successful implementation. Communities effectively reducing readmissions had coalitions with favorable implementation climates, robust stakeholder engagement strategies, and interventions aligned with local concerns and capabilities. The CFIR can help guide, monitor and evaluate community-based improvement initiatives, although further development some constructs is needed.


2020 ◽  
Vol 11 ◽  
pp. 215013272096845
Author(s):  
Arletha Williams-Livingston ◽  
Tabia Henry Akintobi ◽  
Ananya Banerjee

Background: The Morehouse School of Medicine Patient Centered Medical Home and Neighborhood Project was developed to implement a community-based participatory research driven, integrated patient-centered medical home and neighborhood (PCMH) pilot intervention. The purpose of the PCMHN was to develop a care coordination program for underserved, high-risk patients with multiple morbidities served by the Morehouse Healthcare Comprehensive Family Health Clinic. Measures: A community needs assessment, patient surveys and provider interviews were administered. Results: Among a panel of 367 high-risk patients and potential participants, 93 participated in the intervention and 42 patients completed the intervention. The patients self-reported increased utilization of community support, increased satisfaction with health care options, and increased self-care management ability. Conclusion: The results were largely attributable to the efforts of community health workers and targeted community engagement. Lessons learned from implementation and integration of a community-based participatory approach will be used to train clinicians and small practices on how to affect change using a care coordination model for underserved, high-risk patients emphasizing CBPR.


2020 ◽  
Vol 9 (2) ◽  
pp. e000814 ◽  
Author(s):  
Lesley Charles ◽  
Lisa Jensen ◽  
Jacqueline M I Torti ◽  
Jasneet Parmar ◽  
Bonnie Dobbs ◽  
...  

BackgroundImproving transitions in care is a major focus of healthcare planning. The objective of this study was to determine the improvement in transitions from an intervention identifying complex older adult patients in acute care and supporting their discharge into the community.MethodsThis was a quality assurance study evaluating an intervention on high-risk patients admitted in an acute care hospital. In phase 1, the Length of Stay, Acuity of the Admission, Charlson Comorbidity Index Score, and Emergency Department Use (LACE Index) was selected to assess a patient’s risk for readmission and a standard discharge protocol was developed. In phase 2, the intervention was implemented: (1) all patients were screened for the risk of readmission using the LACE Index; and (2) the high-risk patients were provided care coordination including follow-up phone calls focused on medications, equipment and homecare services. Emergency department (ED) revisits and hospital readmissions were measured.ResultsThe LACE Index identified 433/1621 (27%) patients at high risk for readmission. Care coordination was achieved within 72 hours in 79% of patients. The 433 high-risk patients receiving the intervention, compared with a group without intervention (n=231), had lower lengths of stay (12.7 days vs 16.6 days); similar 7-day ED revisits (10.6% vs 10.8%) and 30-day ED revisits (30.5% vs 33.3%); lower 90-day readmissions (39.3% vs 44.6%); and lower 6-month readmissions (50.9% vs 58.4%). The 7-day and 30-day readmissions were similar in both groups.ConclusionsIdentifying complex patients at high risk for readmission and supporting them during transitions from acute care to home potentially decreases lengths of hospital stay and prevents short-term ED revisits and long-term readmissions.


Blood ◽  
2020 ◽  
Vol 136 (Supplement 1) ◽  
pp. 21-22
Author(s):  
F. B. B. Hagemeister ◽  
Anne Jacobson ◽  
Jeffrey D Carter ◽  
Tamar Sapir

Background Using shared decision-making (SDM) to define therapeutic goals and shape individualized treatment plans for relapsed/refractory diffuse large B-cell lymphoma (R/R DLBCL) can profoundly improve patient-reported outcomes. However, unique barriers within oncology systems can impede patient-centered care coordination and delivery. In this quality improvement (QI) initiative, we assessed barriers to patient-centered R/R DLBCL care in 2 community oncology systems and conducted team-based audit-feedback (AF) sessions within each system to facilitate improved care coordination. Methods Between 12/2019 and 1/2020, 33 hematology/oncology healthcare professionals (HCPs) completed team-based surveys designed to assess barriers to quality R/R DLBCL care in 2 community oncology systems (Table 1). In addition, we retrospectively audited electronic medical records (EMRs) of 75 patients with R/R DLBCL to compare documented practice patterns with self-reported survey results. To address identified gaps, 31 HCPs practicing within the 2 systems participated in AF sessions; together, clinical teams developed action plans guided by survey insights and EMR findings. Additional surveys completed before and after the AF sessions measured changes in participants' beliefs and confidence in care delivery. A planned prospective follow-up EMR audit of 75 patients will assess changes in documentation and practice behavior. Results Team Surveys and EMR Audit: Using a 5-point Likert scale (0 = extremely unlikely; 5 = extremely likely), HCPs indicated a high likelihood of using prognostic scores (mean score, 3.8) and cell of origin (mean score, 3.9) to inform DLBCL treatment decisions. However, despite documentation of individual prognostic factors, only 30% of EMRs included the calculated International Prognostic Index (IPI) risk score and only 8% included cell of origin. No EMRs included an age-adjusted IPI, stage-modified IPI, or NCCN-IPI score (Figure 1). Despite only 6% of HCPs identifying engaging patients in SDM as a major practice challenge, SDM resources were consistently underutilized (Figure 2). Only 30% of HCPs estimated using SDM in more than half of their patients; 23% of HCPs reported using SDM with none of their patients with R/R DLBCL. Moreover, while 63% of HCPs reported routinely asking the patient what role he/she wishes to play (active, passive, collaborative), fewer HCPs integrated other SDM tools: using visual aids to communicate treatment benefits/risks (59%), referring patients to online education resources (53%), including the patient's spouse/family members in decisions (47%), and discussing financial toxicity (38%). Small-Group AF Sessions: When asked to identify a single aspect of R/R DLBCL care in greatest need for improvement in their systems, HCPs most commonly selected care coordination (34%) and adverse event recognition/management (20%), followed by individualizing treatment (16%) and prognostic scoring (14%). As part of their action plans, HCPs prioritized 3 practice behaviors to address with their clinical teams: individualizing treatment decisions based on patient- and disease-related factors (40%), improving communication during care transitions (40%), and providing adequate patient education about treatment options and potential side effects (20%). As a result of participating in the AF sessions, HCPs reported a meaningful shift in beliefs about collaborative care: 84% of HCPs agreed or strongly agreed that collaboration across the extended oncology care team is essential for achieving DLBCL treatment goals, an increase from 64% prior to the AF sessions. Further, HCPs reported increased confidence in their ability to perform each of 6 aspects of evidence-based, collaborative, patient-centered care (Figure 3). Conclusions Key system-based barriers to providing individualized R/R DLBCL care include adequate documentation of prognostic factors, care coordination, and effective SDM. After participating in this QI initiative, HCPs demonstrated improved commitment to team-based collaboration and increased confidence in delivering patient-centered care. Remaining practice gaps and challenges can inform future QI programs. Study Sponsor Statement The study reported in this abstract was funded by an independent educational grant from Genentech. The grantor had no role in the study design, execution, analysis, or reporting. Disclosures No relevant conflicts of interest to declare.


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