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  • 1
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    Online Resource
    Risk stratification and daily symptom monitoring for oncology patients.
    American Society of Clinical Oncology (ASCO) ; 2019
    In:  Journal of Clinical Oncology Vol. 37, No. 15_suppl ( 2019-05-20), p. 6535-6535
    Details
    In: Journal of Clinical Oncology, American Society of Clinical Oncology (ASCO), Vol. 37, No. 15_suppl ( 2019-05-20), p. 6535-6535
    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]
    Type of Medium: Online Resource
    ISSN: 0732-183X , 1527-7755
    URL: Article
    DOI: 10.1200/JCO.2019.37.15_suppl.6535
    RVK:
    XA 52760
    RVK:
    XA 10000
    Language: English
    Publisher: American Society of Clinical Oncology (ASCO)
    Publication Date: 2019
    detail.hit.zdb_id: 2005181-5
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  • 2
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    Online Resource
    Building a Clinically Relevant Risk Model: Predicting Risk of a Potentially Preventable Acute Care Visit for Patients Starting Antineoplastic Treatment
    American Society of Clinical Oncology (ASCO) ; 2020
    In:  JCO Clinical Cancer Informatics , No. 4 ( 2020-11), p. 275-289
    Details
    In: JCO Clinical Cancer Informatics, American Society of Clinical Oncology (ASCO), , No. 4 ( 2020-11), p. 275-289
    Abstract: 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.
    Type of Medium: Online Resource
    ISSN: 2473-4276
    URL: Article
    DOI: 10.1200/CCI.19.00104
    Language: English
    Publisher: American Society of Clinical Oncology (ASCO)
    Publication Date: 2020
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  • 3
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    Remote Monitoring and Management of High-Risk Patients Being Started on Antineoplastic Treatment
    JMIR Publications Inc. ; 2019
    In:  Iproceedings Vol. 5, No. 1 ( 2019-10-2), p. e15181-
    Details
    In: Iproceedings, JMIR Publications Inc., Vol. 5, No. 1 ( 2019-10-2), p. e15181-
    Abstract: Suboptimal management of cancer-related symptoms can lead to potentially preventable emergency department visits. Early detection and management of these symptoms leads to improved patient outcomes. The goal of this program is to capture symptom data on a daily cadence from patients beginning started on antineoplastic treatment and make these data available to staff who would intervene with the aim of mitigating symptom escalation. The aims of this pilot were patient acceptance of automated questionnaires, use of data by clinical staff, and integration of digital tools into workflows. Objective The objective was development and initial evaluation of a digitally enabled program to monitor and manage symptoms of cancer patients being started on treatment with antineoplastic drugs. Methods Memorial Sloan Kettering Cancer Center (MSKCC) patients being started on antineoplastic treatment were eligible for inclusion. The technology supporting the program included: (i) a predictive model identifying patients at high risk for emergency department visit in the next 6 months, (ii) a patient portal enabling symptom questionnaires to be sent to enrolled patients, (iii) an internally developed application that allows staff to review and trend symptom data, (iv) “alerts” for concerning symptoms that are sent to a dedicated team of oncology registered nurses and nurse practitioners, and (v) a secure messaging platform for communication between staff and patients. The predictive model runs nightly to identify eligible patients. Physicians review the risk information and decide whether to enroll each patient. Enrolled patients receive a daily patient-reported outcome questionnaire to capture symptoms. Alerts are generated based on the patient’s response to the symptom questions. The team reviews symptom data and interacts with the patient via phone, secure messaging, or televisits. The team collaborates with the primary oncology team as appropriate. Results The program went live in October 2018. Fifteen medical oncologists are participating in the pilot. As of June 21, 2019, 302 patients have been evaluated by the predictive model, of which 53 have been high risk and 249 have been low risk. Physicians have enrolled a total of 86 patients. Enrolled patients have completed 4198 out of 7287 symptom questionnaires, for a completion rate of 57.6%. Of the 4198 questionnaires, 1638 have generated a “red alert” (severe) symptom. Over 1200 secure messages have been exchanged between patients and staff. Lessons learned include: (i) patient acceptance of daily questionnaires has been high, (ii) the use of alerts assists the team in proactively managing participating patients, and (iii) patients and caregivers are reporting that they find the program valuable. The pilot demonstrated that daily symptom monitoring through a technology-enabled platform and a dedicated team is feasible and we are examining how the program might be scaled across our organization. Conclusions Through the use of automated symptom questionnaires and various bidirectional communication channels, remote monitoring and management of symptoms in cancer patients is feasible. This program enables providers at MSKCC to have visibility into patient symptomatology between visits and improves our understanding of the patient’s needs. The workflows of the staff monitoring the symptoms and how best to coordinate symptom management across multiple providers are areas of ongoing refinement.
    Type of Medium: Online Resource
    ISSN: 2369-6893
    URL: Article
    DOI: 10.2196/15181
    Language: English
    Publisher: JMIR Publications Inc.
    Publication Date: 2019
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  • 4
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    InSight Care Pilot Program: Redefining Seeing a Patient
    American Society of Clinical Oncology (ASCO) ; 2020
    In:  JCO Oncology Practice Vol. 16, No. 10 ( 2020-10), p. e1050-e1059
    Details
    In: JCO Oncology Practice, American Society of Clinical Oncology (ASCO), Vol. 16, No. 10 ( 2020-10), p. e1050-e1059
    Abstract: 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.
    Type of Medium: Online Resource
    ISSN: 2688-1527 , 2688-1535
    URL: Article
    DOI: 10.1200/OP.20.00214
    Language: English
    Publisher: American Society of Clinical Oncology (ASCO)
    Publication Date: 2020
    detail.hit.zdb_id: 3005549-0
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  • 5
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    A framework for building a clinically relevant risk model.
    American Society of Clinical Oncology (ASCO) ; 2019
    In:  Journal of Clinical Oncology Vol. 37, No. 15_suppl ( 2019-05-20), p. 6554-6554
    Details
    In: Journal of Clinical Oncology, American Society of Clinical Oncology (ASCO), Vol. 37, No. 15_suppl ( 2019-05-20), p. 6554-6554
    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.
    Type of Medium: Online Resource
    ISSN: 0732-183X , 1527-7755
    URL: Article
    DOI: 10.1200/JCO.2019.37.15_suppl.6554
    RVK:
    XA 52760
    RVK:
    XA 10000
    Language: English
    Publisher: American Society of Clinical Oncology (ASCO)
    Publication Date: 2019
    detail.hit.zdb_id: 2005181-5
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  • 6
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    Online Resource
    Pilot program of remote monitoring for high-risk patients on antineoplastic treatment.
    American Society of Clinical Oncology (ASCO) ; 2020
    In:  Journal of Clinical Oncology Vol. 38, No. 15_suppl ( 2020-05-20), p. 2027-2027
    Details
    In: Journal of Clinical Oncology, American Society of Clinical Oncology (ASCO), Vol. 38, No. 15_suppl ( 2020-05-20), p. 2027-2027
    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]
    Type of Medium: Online Resource
    ISSN: 0732-183X , 1527-7755
    URL: Article
    DOI: 10.1200/JCO.2020.38.15_suppl.2027
    RVK:
    XA 52760
    RVK:
    XA 10000
    Language: English
    Publisher: American Society of Clinical Oncology (ASCO)
    Publication Date: 2020
    detail.hit.zdb_id: 2005181-5
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