Evaluation of the quality of clinical data collection for a pan-Canadian cohort of children affected by inherited metabolic diseases: lessons learned from the Canadian Inherited Metabolic Diseases Research Network

Objectives. Disease surveillance combines data collection and analysis with dissemination of findings to decision makers. The timeliness of these activities affects the ability to implement preventive measures. Influenza surveillance has traditionally been hampered by delays in both data collection and dissemination. Methods. We used statistical process control (SPC) to evaluate the daily percentage of outpatient visits with a positive point-of-care (POC) influenza test in the University of Utah Primary Care Research Network. Results. Retrospectively, POC testing generated an alert in each of 4 seasons (2004–2008, median 16 days before epidemic onset), suggesting that email notification of clinicians would be 9 days earlier than surveillance alerts posted to the Utah Department of Health website. In the 2008-09 season, the algorithm generated a real-time alert 19 days before epidemic onset. Clinicians in 4 intervention clinics received email notification of the alert within 4 days. Compared with clinicians in 6 control clinics, intervention clinicians were 40% more likely to perform rapid testing () and twice as likely to vaccinate for seasonal influenza () after notification. Conclusions. Email notification of SPC-generated alerts provided significantly earlier notification of the epidemic onset than traditional surveillance. Clinician preventive behavior was not significantly different in intervention clinics.

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Predicting health-related quality of life of parents of children with inherited metabolic diseases

Acta Paediatrica ◽

10.1111/j.1651-2227.2009.01269.x ◽

2009 ◽

Vol 98 (7) ◽

pp. 1205-1210 ◽

Cited By ~ 33

Author(s):

Janneke Hatzmann ◽

Marlies J Valstar ◽

Annet M Bosch ◽

Frits A Wijburg ◽

Hugo SA Heymans ◽

...

Keyword(s):

Quality Of Life ◽

Metabolic Diseases ◽

Health Related Quality ◽

Inherited Metabolic Diseases ◽

Related Quality ◽

Health Related

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Patient and observer reported outcome measures to evaluate health-related quality of life in inherited metabolic diseases: a scoping review

Orphanet Journal of Rare Diseases ◽

10.1186/s13023-018-0953-9 ◽

2018 ◽

Vol 13 (1) ◽

Cited By ~ 5

Author(s):

Carlota Pascoal ◽

Sandra Brasil ◽

Rita Francisco ◽

Dorinda Marques-da-Silva ◽

Agnes Rafalko ◽

...

Keyword(s):

Quality Of Life ◽

Outcome Measures ◽

Scoping Review ◽

Metabolic Diseases ◽

Health Related Quality ◽

Inherited Metabolic Diseases ◽

Related Quality ◽

Health Related

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P62 Impact of the Covid-19 pandemic on the SeaSHeL study

BJS Open ◽

10.1093/bjsopen/zrab032.061 ◽

2021 ◽

Vol 5 (Supplement_1) ◽

Author(s):

Nikhil Joshi ◽

Muhammad Ahmed ◽

Rishi Mandavia ◽

Nikhil Joshi ◽

Keyword(s):

Cohort Study ◽

Data Collection ◽

Research Network ◽

Secondary Outcome ◽

Patient Recruitment ◽

Research Approach ◽

Key Factors ◽

National Cohort ◽

The Impact

Abstract Introduction The SeaSHeL Study is an ongoing national, multi-centre, cohort study investigating Sudden Sensorineural Hearing Loss (SSNHL). The study is supported by the INTEGRATE ENT Trainee Research Network and the Audiology NIHR Champions, as well as being adopted onto the NIHR Clinical Research Network. It aims to map the pathway of patients with SSNHL, develop a prognostic model to predict recovery of patients with idiopathic SSNHL and establish the impact on patients’ quality of life. Here we summarise the impact of the Covid-19 pandemic on the study and highlight methods employed to improve data collection. Methods Data collection commenced in October 2019 and as of August 2020, 227 patients have been recruited from 66 registered sites across England. This interim data was analysed. The primary outcome was the change in monthly patient recruitment and site registration. The secondary outcome was the completeness of the dataset. Results Initially, monthly site registration increased to a peak of 31 in December 2019 and monthly patient recruitment increased to a peak of 34 in February 2020. Both levels decreased during the first wave of Covid-19 with 11 patients recruited and 0 sites registered in April 2020. Both levels have been increasing since, with 21 patients recruited and 5 sites registered in August 2020. The dataset of 227 patients has 113 (49.7%) completed records. Conclusions This study represents the largest national cohort study into SSNHL. Despite the Covid-19 pandemic, data collection continued during the first wave and rates are now recovering to pre-Covid-19 levels. Key factors in this recovery are a collaborative research approach involving motivated trainees, an ethics amendment for follow-up data to be collected by telephone and continued engagement of collaborators through regular email correspondence and fortnightly newsletters.

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Generating real-world evidence: Using automated data extraction to replace manual chart review.

Journal of Clinical Oncology ◽

10.1200/jco.2019.37.15_suppl.e18096 ◽

2019 ◽

Vol 37 (15_suppl) ◽

pp. e18096-e18096 ◽

Cited By ~ 1

Author(s):

Jennifer H. Law ◽

Christopher Pettengell ◽

Lisa W Le ◽

Steven Aviv ◽

Patricia DeMarco ◽

...

Keyword(s):

Data Collection ◽

Clinical Data ◽

Real World ◽

Chart Review ◽

Clinical Decision Making ◽

Data Extraction ◽

Clinical Care ◽

Data Abstraction ◽

Real World Evidence

e18096 Background: Real world evidence is a valuable resource to help guide clinical care beyond evidence generated from clinical trials, for example safety and effectiveness of novel treatments in special populations. Administrative databases often lack sufficient clinical detail to address gaps in the improvement of patient management and quality of care. Detailed clinical data collection and curation are resource intensive, limiting the ability to generate and maintain large informative cancer databases. Darwen, novel technology developed by Pentavere, enables the automation of data abstraction from unstructured hospital electronic medical records and may eliminate the need for manual chart review. Methods: Health records were identified through an institutional cancer registry from patients with stage IIIB/IV lung cancer (NSCLC or SCLC) diagnosed and treated at the Princess Margaret Cancer Centre between 01/01/2015 and 31/12/2017. Cases underwent automated data extraction including demographics, comorbidities, treatment, concurrent medications and outcomes until 30/06/2018. Agreement with data fields extracted using manual data collection in an external validation set of patients is planned. Results: Of 1210 patients identified, 538 were eligible for analysis. From automated data abstraction, 9.9% were reported to have SCLC, 67.5% adenocarcinoma, 11.2% squamous carcinoma, 28% EGFR mutations, 5.8% ALK fusions and 9.3% tumour PDL1 > = 50%. Of the 304 (56.5%) that received systemic therapy, initial treatment was chemotherapy for 55.6%, targeted therapy in 34.2% and immunotherapy in 10.2%. Additional outcome data and agreement with manually curated data fields will be presented. Conclusions: Automated software to extract clinical data is a powerful new tool to generate and maintain databases that yield high quality real world clinical evidence. This is a critical next step to improve clinical decision making, inform evidence-based practice and improve quality of cancer care.

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