Obtaining Patient-Reported Outcomes Electronically With “OncoFunction” in Head and Neck Cancer Patients During Aftercare

Abstract

The disease and treatment of patients with head and neck cancer can lead to multiple late and long-term sequelae. Especially pain, psychosocial problems, and voice issues can have a high impact on patients’ health-related quality of life. The aim was to show the feasibility of implementing an electronic Patient-Reported Outcome Measure (PROM) in patients with head and neck cancer (HNC). Driven by our department’s intention to assess Patient-Reported Outcomes (PRO) based on the International Classification of Functioning during tumor aftercare, the program “OncoFunction” has been implemented and continuously refined in everyday practice. The new version of “OncoFunction” was evaluated by 20 head and neck surgeons and radiation oncologists in an interview. From 7/2013 until 7/2017, 846 patients completed the PROM during 2,833 of 3,610 total visits (78.5%). The latest software version implemented newly developed add-ins and increased the already high approval ratings in the evaluation as the number of errors and the time required decreased (6 vs. 0 errors, 1.35 vs. 0.95 min; p<0.01). Notably, patients had different requests using PRO in homecare use. An additional examination shows that only 59% of HNC patients use the world wide web. Using OncoFunction for online-recording and interpretation of PROM improved data acquisition in daily HNC patients’ follow-up. An accessory timeline grants access to former consultations and their visualization supported and simplified structured examinations. This provides an easy-to-use representation of the patient’s functional outcome supporting comprehensive aftercare, considering all aspects of the patient’s life.

Implementation of electronic prospective surveillance models in cancer care: a scoping review

“…While publication years ranged from 2005 to 2021, the majority were published in the last 5 years ( n = 43, 68%). Of the 46 interventions included, nearly half ( n = 22, 48%) were conducted in Europe [ 27 – 48 ], followed by North America ( n = 20, 43%) [ 14 , 15 , 49 – 66 ], Australia ( n = 3, 7%) [ 67 – 69 ], and the Philippines ( n = 1, 2%) [ 70 ]. Most interventions targeted patients with a mix of cancer types ( n = 24, 52%) [ 14 , 15 , 27 , 30 , 35 , 37 , 40 , 41 , 46 , 47 , 51 , 52 , 54 , 55 , 60 – 63 , 65 – 70 ], followed by a focus on head and neck ( n = 4, 8%) [ 31 , 32 , 48 , 53 ], gynecologic ( n = 3, 7%) [ 49 , 57 , 58 ], lung ( n = 3, 7%) [ 39 , 56 , 59 ], and breast ( n = 3, 7%) [ 28 , 42 , 64 ] cancers.…”

“…Of the 46 interventions included, nearly half ( n = 22, 48%) were conducted in Europe [ 27 – 48 ], followed by North America ( n = 20, 43%) [ 14 , 15 , 49 – 66 ], Australia ( n = 3, 7%) [ 67 – 69 ], and the Philippines ( n = 1, 2%) [ 70 ]. Most interventions targeted patients with a mix of cancer types ( n = 24, 52%) [ 14 , 15 , 27 , 30 , 35 , 37 , 40 , 41 , 46 , 47 , 51 , 52 , 54 , 55 , 60 – 63 , 65 – 70 ], followed by a focus on head and neck ( n = 4, 8%) [ 31 , 32 , 48 , 53 ], gynecologic ( n = 3, 7%) [ 49 , 57 , 58 ], lung ( n = 3, 7%) [ 39 , 56 , 59 ], and breast ( n = 3, 7%) [ 28 , 42 , 64 ] cancers. Of the 46 ePSM studies, 33% ( n = 15) explicitly used implementation science in their design, data collection, or analysis [ 30 , 32 , 35 , 41 , 44 , 46 , 50 , 51 , 53 – 55 , 58 , 60 , 67 , 69 ], while 67% ( n = 31) reported on the implementation of an ePSM but did not use an implementation science approach [ 14 , 15 , 27 – 29 , 31 , 33 , 34 , ...…”

Gesundheitsbezogene Lebensqualität bei Stimm- und Aussprachestörungen

“…Die Korrelation zwischen Expertenurteil und die vom Patienten beurteilte Beeinträchtigung war nur moderat [31]. In zunehmendem Maße werden für Befragungen digitale Instrumente eingesetzt, auch bei Patienten mit Kopf-Hals-Tumoren, bis hin zur täglichen Datenerhebung [32,33].…”

THALIS: Human-Machine Analysis of Longitudinal Symptoms in Cancer Therapy

“…Electronic Medical Records (EMR) store patient longitudinal information, often in the form of time series. In general, time-series visualization has utilized point graphs, circle graphs, line graphs [41], parallel coordinate plots [45], or stacked bar charts and their variations [3] to encode time-oriented nominal, ordinal or quantitative data, including in cancer [76,89,104]. For EMR data, Plaisant et al have introduced personal patient summary visualization using timelines [80,93,94], or matrixbased representations [23].…”