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Lipitz-Snyderman A, Chimonas S, Mailankody S, Kim M, Silva N, Kriplani A, Saltz LB, Sihag S, Tan CR, Widmar M, Zauderer M, Weingart S, Perchick W, Roman BR. Clinical value of second opinions in oncology: A retrospective review of changes in diagnosis and treatment recommendations. Cancer Med 2023; 12:8063-8072. [PMID: 36737878 PMCID: PMC10134380 DOI: 10.1002/cam4.5598] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2022] [Revised: 12/08/2022] [Accepted: 12/17/2022] [Indexed: 02/05/2023] Open
Abstract
BACKGROUND Data on the clinical value of second opinions in oncology are limited. We examined diagnostic and treatment changes resulting from second opinions and the expected impact on morbidity and prognosis. METHODS This retrospective cohort study included patients presenting in 2018 to a high-volume cancer center for second opinions about newly diagnosed colorectal, head and neck, lung, and myeloma cancers or abnormal results. Two sub-specialty physicians from each cancer type reviewed 30 medical records (120 total) using a process and detailed data collection guide meant to mitigate institutional bias. The primary outcome measure was the rate of treatment changes that were "clinically meaningful", i.e., expected to impact morbidity and/or prognosis. Among those with treatment changes, another outcome measure was the rate of clinically meaningful diagnostic changes that led to treatment change. RESULTS Of 120 cases, forty-two had clinically meaningful changes in treatment with positive expected outcomes (7 colorectal, 17 head and neck, 11 lung, 7 myeloma; 23-57%). Two patients had negative expected outcomes from having sought a second opinion, with worse short-term morbidity and unchanged long-term morbidity and prognosis. All those with positive expected outcomes had improved expected morbidity (short- and/or long-term); 11 (0-23%) also had improved expected prognosis. Nine involved a shift from treatment to observation; 21 involved eliminating or reducing the extent of surgery, compared to 6 adding surgery or increasing its extent. Of the 42 with treatment changes, 13 were due to clinically meaningful diagnostic changes (1 colorectal, 5 head and neck, 3 lung, 4 myeloma; 3%-17%) . CONCLUSIONS Second-opinion consultations sometimes add clinical value by improving expected prognoses; more often, they offer treatment de-escalations, with corresponding reductions in expected short- and/or long-term morbidity. Future research could identify subgroups of patients most likely to benefit from second opinions.
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Affiliation(s)
- Allison Lipitz-Snyderman
- Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Susan Chimonas
- Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Sham Mailankody
- Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Michelle Kim
- Strategy and Innovation, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Nicholas Silva
- Strategy and Innovation, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Anuja Kriplani
- Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Leonard B Saltz
- Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Smita Sihag
- Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Carlyn Rose Tan
- Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Maria Widmar
- Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Marjorie Zauderer
- Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Saul Weingart
- Rhode Island Hospital and Hasbro Children's Hospital, Providence, Rhode Island, USA
| | - Wendy Perchick
- Strategy and Innovation, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Benjamin R Roman
- Strategy and Innovation, Memorial Sloan Kettering Cancer Center, New York, New York, USA.,Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, New York, USA
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2
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Daly B, Nicholas KJ, Flynn J, Panageas KS, Silva N, Duck E, Zervoudakis A, Holland J, Salvaggio R, Begue A, Wagner I, Sokolowski S, Zablocki M, Chiu YO, Kuperman GJ, Simon BA, Perchick W, Reidy‐Lagunes DL. Association Between Remote Monitoring and Acute Care Visits in High-Risk Patients Initiating Intravenous Antineoplastic Therapy. JCO Oncol Pract 2022; 18:e1935-e1942. [PMID: 36265089 PMCID: PMC9750548 DOI: 10.1200/op.22.00294] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2022] [Revised: 07/26/2022] [Accepted: 08/20/2022] [Indexed: 12/14/2022] Open
Abstract
PURPOSE Traditional oncology care models have not effectively identified and managed at-risk patients to prevent acute care. A next step is to harness advances in technology to enable patients to report symptoms any time, enabling digital hovering-intensive symptom monitoring and management. Our objective was to evaluate a digital platform that identifies and remotely monitors high-risk patients initiating antineoplastic therapy with the goal of preventing acute care visits. METHODS This was a single-institution matched cohort quality improvement study conducted at a National Cancer Institute-designated cancer center between January 1, 2019, and March 31, 2020. Eligible patients were those initiating intravenous antineoplastic therapy who were identified as high risk for seeking acute care. Enrolled patients' symptoms were monitored using a digital platform. A dedicated team of clinicians managed reported symptoms. The primary outcomes of emergency department visits and hospitalizations within 6 months of treatment initiation were analyzed using cumulative incidence analyses with a competing risk of death. RESULTS Eighty-one patients from the intervention arm were matched by stage and disease with contemporaneous high-risk control patients. The matched cohort had similar baseline characteristics. The cumulative incidence of an emergency department visit for the intervention cohort was 0.27 (95% CI, 0.17 to 0.37) at six months compared with 0.47 (95% CI, 0.36 to 0.58) in the control (P = .01) and of an inpatient admission was 0.23 (95% CI, 0.14 to 0.33) in the intervention cohort versus 0.41 (95% CI, 0.30 to 0.51) in the control (P = .02). CONCLUSION The narrow employment of technology solutions to complex care delivery challenges in oncology can improve outcomes and innovate care. This program was a first step in using a digital platform and a remote team to improve symptom care for high-risk patients.
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Affiliation(s)
- Bobby Daly
- Memorial Sloan Kettering Cancer Center, New York, NY
| | | | - Jessica Flynn
- Memorial Sloan Kettering Cancer Center, New York, NY
| | | | | | - Elaine Duck
- Memorial Sloan Kettering Cancer Center, New York, NY
| | | | | | | | - Aaron Begue
- Memorial Sloan Kettering Cancer Center, New York, NY
| | - Isaac Wagner
- Memorial Sloan Kettering Cancer Center, New York, NY
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3
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Daly RM, Nicholas K, Flynn J, Panageas K, Silva N, Duck E, Zervoudakis A, Holland JC, Salvaggio R, Begue A, Wagner I, Sokolowski S, Zablocki M, Chiu YO, Kuperman G, Simon BA, Perchick W, Reidy DL. Association between remote monitoring and acute care visits in high-risk patients initiating intravenous antineoplastic therapy. J Clin Oncol 2022. [DOI: 10.1200/jco.2022.40.16_suppl.1578] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Abstract
1578 Background: Acute care visits (emergency department [ED] visits or inpatient admissions) for patients with cancer are growing disproportionately. Traditional oncology care models have not effectively identified and managed at-risk patients to prevent acute care. A next step is to harness advances in technology and mobile applications to enable patients to report symptoms any time, enabling “digital hovering” - intensive monitoring and management of high-risk patients. Our objective was to evaluate a digital platform that identifies and remotely monitors high-risk patients initiating intravenous antineoplastic therapy with the goal of preventing unnecessary acute care visits. Methods: This was a single-institution matched cohort quality improvement study conducted at an NCI-designated cancer center between January 1, 2019 and March 31, 2020. Eligible patients were those initiating intravenous antineoplastic therapy who were identified as high-risk for seeking acute care. Patients were identified as high-risk for an acute care visit by their oncologist with decision support from a web-based machine learning model. Enrolled patients’ symptoms were monitored using a digital platform. The platform is integrated into the EMR and includes: 1) a secure patient portal enabling communication and daily delivery of electronic patient-reported outcomes symptom assessments; 2) clinical alerts for concerning symptoms; and 3) a symptom trending application. A dedicated team of registered nurses and nurse practitioners managed reported symptoms. These clinicians acted as an extension of the primary oncology team, assisting with patient management exclusively through the platform. The primary outcomes evaluated were incidence of ED visits and inpatient admissions within six months of intravenous antineoplastic initiation. Results: Eighty-one high-risk patients from the intervention arm were matched by stage and disease with contemporaneous high-risk control patients. Matched cohorts had similar baseline characteristics, including age, sex, race, and treatment. ED visits and hospitalizations within six months of treatment initiation were analyzed using cumulative incidence analyses with a competing risk of death. The cumulative incidence of an ED visit for the intervention cohort was 0.27 (95% CI: 0.17, 0.37) at six months compared to 0.47 (95% CI: 0.36, 0.58) in the control group (p = 0.01). The cumulative incidence of an inpatient admission was 0.23 (95% CI: 0.14, 0.33) in the intervention group versus 0.41 (95% CI: 0.30, 0.51) in the control group (p = 0.02). Conclusions: The narrow employment of technology solutions to complex care delivery challenges in oncology can improve outcomes and innovate care. This program was a first step in using a digital platform and a remote team to improve symptom care in the home for high-risk patients.
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Affiliation(s)
| | | | - Jessica Flynn
- Memorial Sloan Kettering Cancer Center, New York, NY
| | | | | | - Elaine Duck
- Memorial Sloan Kettering Cancer Center, New York, NY
| | | | | | | | - Aaron Begue
- Memorial Sloan Kettering Cancer Center, New York, NY
| | - Isaac Wagner
- Memorial Sloan Kettering Cancer Center, New York, NY
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4
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Daly B, Nicholas K, Flynn J, Silva N, Panageas K, Mao JJ, Gazit L, Gorenshteyn D, Sokolowski S, Newman T, Perry C, Wagner I, Zervoudakis A, Salvaggio R, Holland J, Chiu YO, Kuperman GJ, Simon BA, Reidy-Lagunes DL, Perchick W. Analysis of a Remote Monitoring Program for Symptoms Among Adults With Cancer Receiving Antineoplastic Therapy. JAMA Netw Open 2022; 5:e221078. [PMID: 35244701 PMCID: PMC8897754 DOI: 10.1001/jamanetworkopen.2022.1078] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/11/2023] Open
Abstract
IMPORTANCE Electronic patient-reported outcomes (ePROs) may have the potential to improve cancer care delivery by enhancing patient quality of life, reducing acute care visits, and extending overall survival. However, the optimal cadence of ePRO assessments is unknown. OBJECTIVE To determine patient response preferences and the clinical value associated with a daily cadence for ePROs for patients receiving antineoplastic treatment. DESIGN, SETTING, AND PARTICIPANTS This quality improvement study of adult patients undergoing antineoplastic treatment assessed a remote monitoring program using ePROs that was developed to manage cancer therapy-related symptoms. ePRO data submitted between October 16, 2018 to February 29, 2020, from a single regional site within the Memorial Sloan Kettering Cancer Center network were included. Data were analyzed from April 2020 to January 2022. EXPOSURE While undergoing active treatment, patients received a daily ePRO assessment that, based on patient responses, generated yellow (moderate) or red (severe) symptom alerts that were sent to clinicians. MAIN OUTCOMES AND MEASURES The main outcomes assessed included patient response rate, symptom alert frequency, and an analysis of the clinical value of daily ePROs. RESULTS A total of 217 patients (median [range] age, 66 [31-92] years; 103 [47.5%] women and 114 [52.5%] men) initiating antineoplastic therapy at high risk for symptoms were monitored for a median (range) of 91 (2-369) days. Most patients had thoracic (59 patients [27.2%]), head and neck (48 patients [22.1%]), or gastrointestinal (43 patients [19.8%]) malignant neoplasms. Of 14 603 unique symptom assessments completed, 7349 (50.3%) generated red or yellow symptom alerts. Symptoms commonly generating alerts included pain (665 assessments [23.0%]) and functional status (465 assessments [16.1%]). Most assessments (8438 assessments [57.8%]) were completed at home during regular clinic hours (ie, 9 am-5 pm), with higher response rates on weekdays (58.4%; 95% CI, 57.5%-59.5%) than on weekend days (51.3%; 95% CI, 49.5%-53.1%). Importantly, 284 of 630 unique red alerts (45.1%) surfaced without a prior yellow alert for the same symptom within the prior 7 days; symptom severity fluctuated over the course of a week, and symptom assessments generating a red alert were followed by an acute care visit within 7 days 8.7% of the time compared with 2.9% for assessments without a red alert. CONCLUSIONS AND RELEVANCE These findings suggest that daily ePRO assessments were associated with increased insight into symptom management in patients undergoing antineoplastic treatment and symptom alerts were associated with risk of acute care.
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Affiliation(s)
- Bobby Daly
- Memorial Sloan Kettering Cancer Center, New York, New York
| | - Kevin Nicholas
- Memorial Sloan Kettering Cancer Center, New York, New York
| | - Jessica Flynn
- Memorial Sloan Kettering Cancer Center, New York, New York
| | - Nicholas Silva
- Memorial Sloan Kettering Cancer Center, New York, New York
| | | | - Jun J. Mao
- Memorial Sloan Kettering Cancer Center, New York, New York
| | - Lior Gazit
- Memorial Sloan Kettering Cancer Center, New York, New York
| | | | | | | | - Claire Perry
- Memorial Sloan Kettering Cancer Center, New York, New York
| | - Isaac Wagner
- Memorial Sloan Kettering Cancer Center, New York, New York
| | | | - Rori Salvaggio
- Memorial Sloan Kettering Cancer Center, New York, New York
| | - Jessie Holland
- Memorial Sloan Kettering Cancer Center, New York, New York
| | - Yeneat O. Chiu
- Memorial Sloan Kettering Cancer Center, New York, New York
| | | | - Brett A. Simon
- Memorial Sloan Kettering Cancer Center, New York, New York
| | | | - Wendy Perchick
- Memorial Sloan Kettering Cancer Center, New York, New York
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5
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Daly B, Gorenshteyn D, Nicholas KJ, Zervoudakis A, Sokolowski S, Perry CE, Gazit L, Baldwin Medsker A, Salvaggio R, Adams L, Xiao H, Chiu YO, Katzen LL, Rozenshteyn M, Reidy-Lagunes DL, Simon BA, Perchick W, Wagner I. Building a Clinically Relevant Risk Model: Predicting Risk of a Potentially Preventable Acute Care Visit for Patients Starting Antineoplastic Treatment. JCO Clin Cancer Inform 2020; 4:275-289. [PMID: 32213093 DOI: 10.1200/cci.19.00104] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022] Open
Abstract
PURPOSE To create a risk prediction model that identifies patients at high risk for a potentially preventable acute care visit (PPACV). PATIENTS AND METHODS We developed a risk model that used electronic medical record data from initial visit to first antineoplastic administration for new patients at Memorial Sloan Kettering Cancer Center from January 2014 to September 2018. The final time-weighted least absolute shrinkage and selection operator model was chosen on the basis of clinical and statistical significance. The model was refined to predict risk on the basis of 270 clinically relevant data features spanning sociodemographics, malignancy and treatment characteristics, laboratory results, medical and social history, medications, and prior acute care encounters. The binary dependent variable was occurrence of a PPACV within the first 6 months of treatment. There were 8,067 observations for new-start antineoplastic therapy in our training set, 1,211 in the validation set, and 1,294 in the testing set. RESULTS A total of 3,727 patients experienced a PPACV within 6 months of treatment start. Specific features that determined risk were surfaced in a web application, riskExplorer, to enable clinician review of patient-specific risk. The positive predictive value of a PPACV among patients in the top quartile of model risk was 42%. This quartile accounted for 35% of patients with PPACVs and 51% of potentially preventable inpatient bed days. The model C-statistic was 0.65. CONCLUSION Our clinically relevant model identified the patients responsible for 35% of PPACVs and more than half of the inpatient beds used by the cohort. Additional research is needed to determine whether targeting these high-risk patients with symptom management interventions could improve care delivery by reducing PPACVs.
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Affiliation(s)
- Bobby Daly
- Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY.,Department of Strategy and Innovation, Memorial Sloan Kettering Cancer Center, New York, NY
| | - Dmitriy Gorenshteyn
- Department of Strategy and Innovation, Memorial Sloan Kettering Cancer Center, New York, NY
| | - Kevin J Nicholas
- Department of Strategy and Innovation, Memorial Sloan Kettering Cancer Center, New York, NY
| | - Alice Zervoudakis
- Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY
| | - Stefania Sokolowski
- Department of Strategy and Innovation, Memorial Sloan Kettering Cancer Center, New York, NY
| | - Claire E Perry
- Department of Strategy and Innovation, Memorial Sloan Kettering Cancer Center, New York, NY
| | - Lior Gazit
- Department of Strategy and Innovation, Memorial Sloan Kettering Cancer Center, New York, NY
| | | | - Rori Salvaggio
- Department of Nursing, Memorial Sloan Kettering Cancer Center, New York, NY
| | - Lynn Adams
- Department of Advanced Practice Providers, Memorial Sloan Kettering Cancer Center, New York, NY
| | - Han Xiao
- Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY
| | - Yeneat O Chiu
- Department of Strategy and Innovation, Memorial Sloan Kettering Cancer Center, New York, NY
| | - Lauren L Katzen
- Department of Strategy and Innovation, Memorial Sloan Kettering Cancer Center, New York, NY
| | - Margarita Rozenshteyn
- Department of Strategy and Innovation, Memorial Sloan Kettering Cancer Center, New York, NY
| | | | - Brett A Simon
- Department of Anesthesiology and Critical Care, Memorial Sloan Kettering Cancer Center, New York, NY
| | - Wendy Perchick
- Office of the Executive Vice President, Memorial Sloan Kettering Cancer Center, New York, NY
| | - Isaac Wagner
- Department of Strategy and Innovation, Memorial Sloan Kettering Cancer Center, New York, NY
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6
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Aviki E, Sokolowski S, Leitao M, Liebhaber A, Blinder V, Doyle S, Johari S, Chi A, Esselen K, Broach V, Brown C, Chi D, Jewell E, Roche KL, Mueller J, Sonoda Y, Zivanovic O, Perchick W, Gardner G, Abu-Rustum N. Risk factors for financial toxicity in gynecologic cancer patients receiving treatment. Gynecol Oncol 2020. [DOI: 10.1016/j.ygyno.2020.06.131] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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7
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Daly B, Kuperman G, Zervoudakis A, Baldwin Medsker A, Roy A, Ro AS, Arenas J, Yanamandala HV, Kottamasu R, Salvaggio R, Holland J, Hirsch S, Walters CB, Lauria T, Chow K, Begue A, Rozenshteyn M, Zablocki M, Dhami AK, Silva N, Brown E, Katzen LL, Chiu YO, Perry C, Sokolowski S, Wagner I, Veach SR, Grisham RN, Dang CT, Reidy-Lagunes DL, Simon BA, Perchick W. InSight Care Pilot Program: Redefining Seeing a Patient. JCO Oncol Pract 2020; 16:e1050-e1059. [PMID: 32468925 DOI: 10.1200/op.20.00214] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022] Open
Abstract
PURPOSE Early detection and management of symptoms in patients with cancer improves outcomes. However, the optimal approach to symptom monitoring and management is unknown. InSight Care is a mobile health intervention that captures symptom data and facilitates patient-provider communication to mitigate symptom escalation. PATIENTS AND METHODS Patients initiating antineoplastic treatment at a Memorial Sloan Kettering regional location were eligible. Technology supporting the program included the following: a predictive model that identified patient risk for a potentially preventable acute care visit; a secure patient portal enabling communication, televisits, and daily delivery of patient symptom assessments; alerts for concerning symptoms; and a symptom-trending application. The main outcomes of the pilot were feasibility and acceptability evaluated through enrollment and response rates and symptom alerts, and perceived value evaluated on the basis of qualitative patient and provider interviews. RESULTS The pilot program enrolled 100 high-risk patients with solid tumors and lymphoma (29% of new treatment starts v goal of 25%). Over 6 months of follow-up, the daily symptom assessment response rate was 56% (the goal was 50%), and 93% of patients generated a severe symptom alert. Patients and providers perceived value in the program, and archetypes were developed for program improvement. Enrolled patients were less likely to use acute care than were other high-risk patients. CONCLUSION InSight Care was feasible and holds the potential to improve patient care and decrease facility-based care. Future work should focus on optimizing the cadence of patient assessments, the workforce supporting remote symptom management, and the return of symptom data to patients and clinical teams.
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Affiliation(s)
- Bobby Daly
- Memorial Sloan Kettering Cancer Center, New York, NY
| | | | | | | | - Ankita Roy
- Memorial Sloan Kettering Cancer Center, New York, NY
| | - Alice S Ro
- Memorial Sloan Kettering Cancer Center, New York, NY
| | | | | | - Raj Kottamasu
- Memorial Sloan Kettering Cancer Center, New York, NY
| | | | | | | | | | - Tara Lauria
- Memorial Sloan Kettering Cancer Center, New York, NY
| | - Kim Chow
- Memorial Sloan Kettering Cancer Center, New York, NY
| | - Aaron Begue
- Memorial Sloan Kettering Cancer Center, New York, NY
| | | | | | | | | | - Emily Brown
- Memorial Sloan Kettering Cancer Center, New York, NY
| | | | - Yeneat O Chiu
- Memorial Sloan Kettering Cancer Center, New York, NY
| | - Claire Perry
- Memorial Sloan Kettering Cancer Center, New York, NY
| | | | - Isaac Wagner
- Memorial Sloan Kettering Cancer Center, New York, NY
| | | | | | - Chau T Dang
- Memorial Sloan Kettering Cancer Center, New York, NY
| | | | - Brett A Simon
- Memorial Sloan Kettering Cancer Center, New York, NY
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8
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Daly RM, Kuperman G, Zervoudakis A, Ro A, Roy A, Baldwin A, Salvaggio R, Holland JC, Chow K, Lauria T, Rozenshteyn M, Zablocki M, Chiu YO, Silva N, Perry C, Sokolowski S, Wagner I, Simon BA, Reidy DL, Perchick W. Pilot program of remote monitoring for high-risk patients on antineoplastic treatment. J Clin Oncol 2020. [DOI: 10.1200/jco.2020.38.15_suppl.2027] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Abstract
2027 Background: Early detection and management of symptoms in patients with cancer improves outcomes, however, the optimal approach to symptom monitoring and management is unknown. This pilot program uses a mobile health intervention to capture and make accessible symptom data for high-risk patients to mitigate symptom escalation. Methods: Patients initiating antineoplastic treatment at a Memorial Sloan Kettering regional location were eligible. A dedicated staff of RNs and nurse practitioners managed the patients remotely. The technology supporting the program included: 1) a predictive model that identified patients at high risk for a potentially preventable acute care visit; 2) a patient portal enabling daily ecological momentary assessments (EMA); 3) alerts for concerning symptoms; 4) an application that allowed staff to review and trend symptom data; and 5) a secure messaging platform to support communications and televisits between staff and patients. Feasibility and acceptability were evaluated through enrollment (goal ≥25% of new treatment starts) and response rates (completion of > 50% of daily symptom assessments); symptom alerts; perceived value based on qualitative interviews with patients and providers; and acute care usage. Results: Between October 15, 2018 and July 10, 2019, the pilot enrolled 100 high-risk patients with solid tumors and lymphoma initiating antineoplastic treatment (median age: 66 years, 45% female). This represented 29% of patients starting antineoplastics. Over six months of follow-up, the response rate to the daily assessments was 56% and 93% of patients generated a severe symptom alert (Table). Both patients and providers perceived value in the program and 5,010 symptom-related secure messages were shared between staff and enrolled patients during the follow-up period. There was a preliminary signal in acute care usage with a 17% decrease in ED visits compared to a cohort of high-risk unenrolled patients. Conclusions: This pilot program of intensive monitoring of high-risk patients is feasible and holds significant potential to improve patient care and decrease hospital resources. Future work should focus on the optimal cadence of EMAs, the workforce to support remote symptom management, and how best to return symptom data to patients and clinical teams. [Table: see text]
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Affiliation(s)
| | | | | | - Alice Ro
- Memorial Sloan Kettering Cancer Center, New York, NY
| | - Ankita Roy
- Memorial Sloan Kettering Cancer Center, New York, NY
| | | | | | | | - Kimberly Chow
- Memorial Sloan Kettering Cancer Center, New York, NY
| | - Tara Lauria
- Memorial Sloan Kettering Cancer Center, New York, NY
| | | | | | | | | | - Claire Perry
- Memorial Sloan Kettering Cancer Center, New York, NY
| | | | - Isaac Wagner
- Memorial Sloan Kettering Cancer Center, New York, NY
| | - Brett A Simon
- Memorial Sloan Kettering Cancer Center, New York, NY
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9
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Daly RM, Gorenshteyn D, Gazit L, Sokolowski S, Nicholas K, Perry C, Adams L, Baldwin A, Holland JC, Zervoudakis A, Xiao H, Salvaggio R, Chiu YO, Katzen LL, Rozenshteyn M, Reidy DL, Simon BA, Perchick W, Wagner I. A framework for building a clinically relevant risk model. J Clin Oncol 2019. [DOI: 10.1200/jco.2019.37.15_suppl.6554] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Abstract
6554 Background: Acute care accounts for half of cancer expenditures and is a measure of poor quality care. Identifying patients at high risk for emergency department (ED) visits enables institutions to target resources to those most likely to benefit. Risk stratification models developed to date have not been meaningfully employed in oncology, and there is a need for clinically relevant models to improve patient care. Methods: We established and applied a predictive framework for clinical use with attention to modeling technique, clinician feedback, and application metrics. The model employs electronic health record data from initial visit to first antineoplastic administration for patients at our institution from January 2014 to June 2017. The binary dependent variable is occurrence of an ED visit within the first 6 months of treatment. The final regularized multivariable logistic regression model was chosen based on clinical and statistical significance. In order to accommodate for the needs to the program, parameter selection and model calibration were optimized to suit the positive predictive value of the top 25% of observations as ranked by model-determined risk. Results: There are 5,752 antineoplastic administration starts in our training set, and 1,457 in our test set. The positive predictive value of this model for the top 25% riskiest new start antineoplastic patients is 0.53. From over 1,400 data features, the model was refined to include 400 clinically relevant ones spanning demographics, pathology, clinician notes, labs, medications, and psychosocial information. At the patient level, specific features determining risk are surfaced in a web application, RiskExplorer, to enable clinician review of individual patient risk. This physician facing application provides the individual risk score for the patient as well as their quartile of risk when compared to the population of new start antineoplastic patients. For the top quartile of patients, the risk for an ED visit within the first 6 months of treatment is greater than or equal to 49%. Conclusions: We have constructed a framework to build a clinically relevant risk model. We are now piloting it to identify those likely to benefit from a home-based, digital symptom management intervention.
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Affiliation(s)
| | | | - Lior Gazit
- Memorial Sloan Kettering Cancer Center, New York, NY
| | | | | | - Claire Perry
- Memorial Sloan Kettering Cancer Center, New York, NY
| | - Lynn Adams
- Memorial Sloan Kettering Cancer Center, New York, NY
| | | | | | | | - Han Xiao
- Memorial Sloan Kettering Cancer Center, Basking Ridge, NJ
| | | | | | | | | | | | - Brett A Simon
- Memorial Sloan Kettering Cancer Center, New York, NY
| | | | - Isaac Wagner
- Memorial Sloan Kettering Cancer Center, New York, NY
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10
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Daly RM, Perry C, Chiu YO, Katzen LL, Rozenshteyn M, Kuperman G, Dhami AK, Salvaggio R, Baldwin A, Holland JC, Chow K, Dang CT, Grisham RN, Veach SR, Zervoudakis A, Wagner I, Simon BA, Reidy DL, Perchick W. Risk stratification and daily symptom monitoring for oncology patients. J Clin Oncol 2019. [DOI: 10.1200/jco.2019.37.15_suppl.6535] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Abstract
6535 Background: Monitoring and managing patient reported outcomes (PROs) has been recommended for oncology patients on active treatment but can be time and resource intensive. Identifying patients likely to benefit and the optimal frequency of PRO capture is still under investigation. We tested the feasibility of monitoring patients who are high-risk risk for acute care with daily PROs. Methods: Using data from our institution, we developed a model that employs over 400 clinical variables to calculate a patient’s risk of an emergency room visit within 6 months following the onset of treatment. From October 15, 2018 to January 23, 2019, we enrolled patients identified as high risk through a technology-enabled program to monitor and manage those patients’ symptoms. Enrolled patients entered PRO assessments daily via an online portal. Symptoms were monitored and managed by a centralized clinical team. Tiered notifications informed the team of concerning or escalating symptoms. We assessed how frequently patients completed symptom assessments and the frequency of symptom notifications. Results: During the pilot, 28 patients were identified as high risk and enrolled in the program (median age 65; 64% percent female). Disease types were: 15 (54%) thoracic, 7 (25%) gynecologic, 6 (21%) gastrointestinal. Median time in the program was 50 (6-98) days. Patients completed 840 of 1,350 assessments (62%). There were 328 assessments that triggered moderate alerts (39%) and 220 that triggered severe alerts (26%). The table describes the prevalence of symptoms at the patient-level. Conclusions: A model can be employed to identify high-risk patients in collaboration with clinicians. Our adherence rate with a daily symptom assessment was similar to those found in studies of less frequent PRO capture. Future work will expand to a larger patient population with other cancer types, evaluate impact on outcomes, and assess optimal frequency for PRO collection and alert thresholds. [Table: see text]
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Affiliation(s)
| | - Claire Perry
- Memorial Sloan Kettering Cancer Center, New York, NY
| | | | | | | | | | | | | | | | | | - Kimberly Chow
- Memorial Sloan Kettering Cancer Center, New York, NY
| | - Chau T. Dang
- Memorial Sloan Kettering Cancer Center, New York, NY
| | - Rachel N. Grisham
- Memorial Sloan Kettering Cancer Center and Weill Cornell Medical College, New York, NY
| | | | | | - Isaac Wagner
- Memorial Sloan Kettering Cancer Center, New York, NY
| | - Brett A Simon
- Memorial Sloan Kettering Cancer Center, New York, NY
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11
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Daly RM, Gorenshteyn D, Gazit L, Sokolowski S, Nicholas K, Perry C, Adams L, Baldwin A, Katzen LL, Chiu YO, Reidy DL, Simon BA, Perchick W, Wagner I. Employing electronic health record data to predict risk of ED visits for new patients. J Clin Oncol 2018. [DOI: 10.1200/jco.2018.36.34_suppl.144] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Abstract
144 Background: Acute care accounts for half of cancer expenditures and is a measure of poor quality care. Identifying patients at high risk for ED visits enables institutions to target symptom management resources to those most likely to benefit. Risk stratification models developed to date have not been meaningfully employed in oncology, and there is a need for clinically relevant models to improve patient care. Methods: We established a predictive analytics framework for clinical use with attention to the modeling technique, clinician feedback, and application metrics. The model employs EHR data from initial visit to first antineoplastic administration for new patients at our institution from January 2014 to June 2017. The binary dependent variable is occurrence of an ED visit within the first 6 months of treatment. From over 1,400 data features, the model was refined to include 400 clinically relevant ones spanning demographics, pathology, clinician notes, labs, medications, and psychosocial information. Clinician review was performed to confirm EHR data input validity. The final regularized multivariate logistic regression model was chosen based on clinical and statistical significance. Parameter selection and model evaluation utilized the positive predictive value for the top 25% of observations ranked by model-determined risk. The final model was evaluated using a test set containing 20% of randomly held out data. The model was calibrated based on a 5-fold cross-validation scheme over the training set. Results: There are 5,752 antineoplastic starts in our training set, and 1,457 in our test set. The positive predictive value of this model for the top 25% riskiest new start antineoplastic patients is 0.53. The 400 clinically relevant features draw from multiple areas in the EHR. For example, those features found to increase risk include: combination chemotherapy, low albumin, social work needs, and opioid use, whereas those found to decrease risk include stage 1 disease, never smoker status, and oral antineoplastic therapy. Conclusions: We have constructed a framework to build a clinically relevant model. We are now piloting it to identify those likely to benefit from a home-based, digital symptom management intervention.
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Affiliation(s)
| | | | - Lior Gazit
- Memorial Sloan Kettering Cancer Center, New York, NY
| | | | | | - Claire Perry
- Memorial Sloan Kettering Cancer Center, New York, NY
| | - Lynn Adams
- Memorial Sloan Kettering Cancer Center, New York, NY
| | | | | | | | | | - Brett A Simon
- Memorial Sloan Kettering Cancer Center, New York, NY
| | | | - Isaac Wagner
- Memorial Sloan Kettering Cancer Center, New York, NY
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12
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Daly RM, Gorenshteyn D, Gazit L, Sokolowski S, Nicholas K, Perry C, Adams L, Baldwin A, Katzen LL, Chiu YO, Reidy DL, Simon BA, Perchick W, Wagner I. Employing electronic health record data to predict risk of emergency department visits for new patients. J Clin Oncol 2018. [DOI: 10.1200/jco.2018.36.30_suppl.314] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Abstract
314 Background: Acute care accounts for half of cancer expenditures and is a measure of poor quality care. Identifying patients at high risk for ED visits enables institutions to target symptom management resources to those most likely to benefit. Risk stratification models developed to date have not been meaningfully employed in oncology, and there is a need for clinically relevant models to improve patient care. Methods: We established a predictive analytics framework for clinical use with attention to the modeling technique, clinician feedback, and application metrics. The model employs EHR data from initial visit to first antineoplastic administration for new patients at our institution from January 2014 to June 2017. The binary dependent variable is occurrence of an ED visit within the first 6 months of treatment. From over 1,400 data features, the model was refined to include 400 clinically relevant ones spanning demographics, pathology, clinician notes, labs, medications, and psychosocial information. Clinician review was performed to confirm EHR data input validity. The final regularized multivariate logistic regression model was chosen based on clinical and statistical significance. Parameter selection and model evaluation utilized the positive predictive value for the top 25% of observations ranked by model-determined risk. The final model was evaluated using a test set containing 20% of randomly held out data. The model was calibrated based on a 5-fold cross-validation scheme over the training set. Results: There are 5,752 antineoplastic starts in our training set, and 1,457 in our test set. The positive predictive value of this model for the top 25% riskiest new start antineoplastic patients is 0.53. The 400 clinically relevant features draw from multiple areas in the EHR. For example, those features found to increase risk include: combination chemotherapy, low albumin, social work needs, and opioid use, whereas those found to decrease risk include stage 1 disease, never smoker status, and oral antineoplastic therapy. Conclusions: We have constructed a framework to build a clinically relevant model. We are now piloting it to identify those likely to benefit from a home-based, digital symptom management intervention.
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Affiliation(s)
| | | | - Lior Gazit
- Memorial Sloan Kettering Cancer Center, New York, NY
| | | | | | - Claire Perry
- Memorial Sloan Kettering Cancer Center, New York, NY
| | - Lynn Adams
- Memorial Sloan Kettering Cancer Center, New York, NY
| | | | | | | | | | - Brett A Simon
- Memorial Sloan Kettering Cancer Center, New York, NY
| | | | - Isaac Wagner
- Memorial Sloan Kettering Cancer Center, New York, NY
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13
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Daly B, Nicholas K, Gorenshteyn D, Sokolowski S, Gazit L, Adams L, Matays J, Katzen LL, Chiu YO, Xiao H, Salvaggio R, Baldwin-Medsker A, Chow K, Nelson J, Ross M, Ng KK, Zervoudakis A, Perchick W, Reidy DL, Simon BA, Wagner I. Misery Loves Company: Presenting Symptom Clusters to Urgent Care by Patients Receiving Antineoplastic Therapy. J Oncol Pract 2018; 14:e484-e495. [PMID: 30016125 DOI: 10.1200/jop.18.00199] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022] Open
Abstract
PURPOSE The Centers for Medicare & Medicaid Services (CMS) identifies suboptimal management of treatment toxicities as a care gap and proposes the measurement of hospital performance on the basis of emergency department visits for 10 common symptoms. Current management strategies do not address symptom co-occurrence. METHODS We evaluated symptom co-occurrence in three patient cohorts that presented to a cancer hospital urgent care center in 2016. We examined both the CMS-identified symptoms and an expanded clinician-identified set defined as symptoms that could be safely managed in the outpatient setting if identified early and managed proactively. The cohorts included patients who presented with a CMS-defined symptom within 30 days of treatment, patients who presented within 30 days of treatment with a symptom from the expanded set, and patients who presented with a symptom from the expanded set within 30 days of treatment start. Symptom co-occurrence was measured by Jaccard index. A community detection algorithm was used to identify symptom clusters on the basis of a random walk process, and network visualizations were used to illustrate symptom dynamics. RESULTS There were 6,429 presentations in the CMS symptom-defined cohort. The network analysis identified two distinct symptom clusters centered around pain and fever. In the expanded symptom cohort, there were 5,731 visits and six symptom clusters centered around fever, emesis/nausea, fatigue, deep vein thrombosis, pain, and ascites. For patients who newly initiated treatment, there were 1,154 visits and four symptom clusters centered around fever, nausea/emesis, fatigue, and deep vein thrombosis. CONCLUSION Uncontrolled symptoms are associated with unplanned acute care. Recognition of the complexity of symptom co-occurrence can drive improved management strategies.
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Affiliation(s)
- Bobby Daly
- Memorial Sloan Kettering Cancer Center, New York, NY
| | | | | | | | - Lior Gazit
- Memorial Sloan Kettering Cancer Center, New York, NY
| | - Lynn Adams
- Memorial Sloan Kettering Cancer Center, New York, NY
| | - Jennie Matays
- Memorial Sloan Kettering Cancer Center, New York, NY
| | | | - Yeneat O Chiu
- Memorial Sloan Kettering Cancer Center, New York, NY
| | - Han Xiao
- Memorial Sloan Kettering Cancer Center, New York, NY
| | | | | | - Kimberly Chow
- Memorial Sloan Kettering Cancer Center, New York, NY
| | - Judith Nelson
- Memorial Sloan Kettering Cancer Center, New York, NY
| | - Mikel Ross
- Memorial Sloan Kettering Cancer Center, New York, NY
| | - Kenneth K Ng
- Memorial Sloan Kettering Cancer Center, New York, NY
| | | | | | - Diane L Reidy
- Memorial Sloan Kettering Cancer Center, New York, NY
| | - Brett A Simon
- Memorial Sloan Kettering Cancer Center, New York, NY
| | - Isaac Wagner
- Memorial Sloan Kettering Cancer Center, New York, NY
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14
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Daly RM, Nicholas K, Gorenshteyn D, Sokolowski S, Gazit L, Adams L, Matays J, Katzen LL, Chiu OO, Xiao H, Salvaggio R, Baldwin A, Chow K, Ross M, Ng KK, Zervoudakis A, Perchick W, Reidy DL, Simon BA, Wagner I. Emergency department (ED) presenting symptom clusters for chemotherapy patients. J Clin Oncol 2018. [DOI: 10.1200/jco.2018.36.15_suppl.e18509] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Affiliation(s)
| | - Kevin Nicholas
- Memorial Sloan Kettering Cancer Center, New York, NY, US
| | | | | | - Lior Gazit
- Memorial Sloan Kettering Cancer Center, New York, NY
| | - Lynn Adams
- Memorial Sloan Kettering Cancer Center, New York, NY, US
| | - Jennie Matays
- Memorial Sloan Kettering Cancer Center, New York, NY
| | | | | | - Han Xiao
- Memorial Sloan Kettering Cancer Center, Basking Ridge, NJ
| | | | | | - Kimberly Chow
- Memorial Sloan Kettering Cancer Center, New York, NY
| | - Mikel Ross
- Memorial Sloan Kettering Cancer Center, New York, NY
| | - Kenneth K. Ng
- Memorial Sloan Kettering Cancer Center, Rockville Centre, NY
| | | | | | | | - Brett A Simon
- Memorial Sloan Kettering Cancer Center, New York, NY
| | - Isaac Wagner
- Memorial Sloan Kettering Cancer Center, New York, NY
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Krebs P, Burkhalter J, Lewis S, Hendrickson T, Chiu O, Fearn P, Perchick W, Ostroff J. Development of a Virtual Reality Coping Skills Game to Prevent Post-Hospitalization Smoking Relapse in Tobacco Dependent Cancer Patients. J Virtual Worlds Res 2009; 2:470. [PMID: 28736598 PMCID: PMC5520623] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
Abstract
Many hospitalized smokers return to smoking after hospital discharge even though continued smoking can compromise treatment effectiveness, reduce survival, increase risk of disease recurrence, and impair quality of life. After leaving a smoke-free hospital, patients encounter smoking cues at home, such as family members who smoke or emotional triggers such as stress, which can elicit powerful urges to smoke and lead to smoking relapse. Enabling smokers to experience such urges in a controlled setting while providing the ability to practice coping skills may be a useful strategy for building quitting self-efficacy. We are developing a virtual reality coping skills (VRCS) game to help hospitalized smokers practice coping strategies to manage these triggers in preparation for returning home after hospitalization. Our multidisciplinary team developed a prototype VRCS game using Second Life, a platform that allowed rapid construction of a virtual reality environment. The prototype contains virtual home spaces (e.g., living room, kitchen) populated with common triggers to smoke and a "toolkit" with scripted actions that enable the avatar to rehearse various coping strategies. Since eliciting and managing urges to smoke is essential to the game's utility as an intervention, we assessed the ability of the prototype virtual environment to engage former smokers in these scenarios. We recruited eight former smokers with a recent history of hospitalization and guided each through a VRCS scenario during which we asked the patient to evaluate the strength of smoking urges and usefulness of coping strategies. Initial data indicate that patients report high urges to smoke (mean = 8.8 on a 10 point scale) when their avatar confronted virtual triggers such as drinking coffee. Patients rated virtual practice of coping strategies, such as drinking water or watching TV, as very helpful (mean = 8.4 on a 10 point scale) in reducing these urges. With further development, this VRCS game may have potential to provide low-cost, effective behavioral rehearsal to prevent relapse to smoking in hospitalized patients.
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Krebs P, Burkhalter J, Lewis S, Hendrickson T, Chiu O, Fearn P, Perchick W, Ostroff J. Development of a Virtual Reality Coping Skills Game to Prevent Post-Hospitalization Smoking Relapse in Tobacco Dependent Cancer Patients. ACTA ACUST UNITED AC 2009. [DOI: 10.4101/jvwr.v2i2.470] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
Abstract
Many smokers hospitalized for cancer-related surgery return to smoking after discharge even though continued smoking can compromise treatment effectiveness, reduce survival, increase risk of disease recurrence, and impair quality of life. After leaving the smoke-free hospital, patients encounter smoking cues at home, i.e., family members who smoke or emotional triggers such as stress that can elicit powerful urges to smoke and lead to smoking relapse. Enabling smokers to experience such urges in a controlled setting while providing the ability to practice coping skills can build quitting self-efficacy. We developed a virtual reality coping skills (VRCS) game to help hospitalized smokers practice coping strategies to manage these triggers in preparation for returning home after hospitalization. Our multidisciplinary team developed the prototype VRCS game using Second Life, a platform that allowed rapid development and functionality. The prototype uses virtual home spaces (e.g., living room, kitchen) populated with common triggers to smoke. The patient uses a “toolkit” with scripted actions that enable the avatar to test out coping strategies. Since eliciting urges to smoke is essential to the game’s efficacy, we are assessing whether the virtual smoking trigger scenarios elicit urges to smoke with 8 cancer patients with a history of smoking. We guided each patient through a VRCS scenario during which we asked the patient to evaluate urges and coping. Initial data indicate that patients report high urges to smoke (7-10 on a 10 point scale) when their patient avatar confronts virtual triggers such as drinking coffee. Patients rated virtual practice of coping strategies, such as drinking water or watching TV, as very helpful (7-10 on a 10 point scale) in reducing these urges. With further development, this VRCS game has potential to provide low-cost, effective behavioral rehearsal to prevent relapse to smoking in hospitalized cancer patients.
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Flehinger HJ, Melamed MR, Zaman MB, Heelan RT, Perchick W, Martini N. Resectability of lung cancer and survival in the New York Lung Cancer Detection Program. World J Surg 1981; 5:681-7. [PMID: 7331361 DOI: 10.1007/bf01657927] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/24/2023]
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