Neoplasms

All Conditions

Head and Neck Neoplasms

All Drugs and Chemicals

Immunomodulating Agents

To describe median OS by primary tumor location (oral cavity, oropharynx, larynx, and hypopharynx) in HNSCC patients after stratification for prognostic factors, including tumor stage and treatment.

To describe clinical characteristics and treatments and to compare OS in locally advanced HNSCC patients

To describe the epidemiologic and clinical characteristics, and treatment and the impact of introducing immunotherapy in recurrent or metastatic HNSCC

All the groups will be descriptive, there is not intervention, as it is an Observational study applying artificial Intelligence (RWE).

No intervention - Just description and predictive models

Miren Taberna, MD,PhD

Miren Taberna

John Almeida

Sujith Baliaga

David Casadevall

Melvin Chua

Andreas Dietz

Robert Ferris

Raul Giglio

Chris Holsinger

Kate Hutcheson

Husham Menhanna

Pablo Parente

Sandro Porceddu

Christian Simon

This will be an international, multicenter, retrospective, observational, and data-driven study using secondary data captured in EHRs. The extraction of the data captured in the EHRs will be performed with SAVANA's EHRead®, an innovative data-driven system based on Natural Language Processing (NLP) and machine learning. For all patients, the Index Date is defined as the timepoint within the study period when they fulfill ALL inclusion criteria and no exclusion criteria. Follow-up comprises the period between Index Date and the last EHR available within the study period. Additional variable-specific time windows may be considered to optimize data collection.

The present study aims to describe the clinical characteristics of patients with HNSCC in a real-world setting by analyzing readily available information in the Electronic Health Records (EHRs). This study will gain a deep insight of the clinical characteristics and real-world outcomes of patients with all stages (early, locally advanced, and metastatic) of HNSCC. It will focus on developing two predictive models to apply in the clinical setting, one for electing patients with high-risk of recurrence after radical treatment, and the second one for selecting recurrent or metastatic patients who could benefit from immunotherapy.

To achieve the proposed study objectives we will use SAVANA´s EHRead® (11-15), a technology that applies Natural Language Processing (NLP) (16) and machine learning to extract, organize, and analyze the unstructured clinical information jotted down by health professionals in patients' EHRs.

Primary objectives

* To develop a predictive model based on dynamic risk stratification (DRS) for the risk of recurrence or disease progression following a primary curative treatment in HNSCC patients with early and locally advanced disease.
* To develop a predictive model based on dynamic risk stratification (DRS) aimed at identifying patients' features that predict long-term survival after immunotherapy in recurrent and metastatic HNSCC patients. Secondary objectives
* To describe median OS by primary tumor location (oral cavity, oropharynx, larynx, and hypopharynx) in HNSCC patients after stratification for prognostic factors, including tumor stage and treatment.
* To describe the demographics, clinical characteristics, and treatment of patients with HNSCC in early and locally advanced stages of the disease.
* To describe the patterns of follow-up in patients with HNSCC in early and locally advanced stages of the disease.
* Departments in charge
* Number of visits
* Imaging and anatomopathological tests
* Recurrence detected by physical examination.
* To evaluate the impact of treatments on patients with locally advanced stages of the disease.
* Patients' early and late toxicity to the treatment, comparing between radiotherapy (+/-cisplatin or cetuximab) vs surgery and post-operative r\< radiotherapy (+/- cisplatin).
* Healthcare resource utilization (HCRU), including medical visits, diagnostics, and hospitalizations.
* To compare OS in locally advanced HNSCC patients (including both HPV+ and HPV- oropharyngeal patients) treated with cisplatin-radiotherapy vs cetuximab-radiotherapy and treated with surgery vs. conservative treatment.
* To compare the demographic and clinical characteristics of exceptional responders and poor responders (based on recurrence and long-term survival). This analysis will be performed independently for HPV+ and HPV- oropharyngeal patients.

Towards A Better Paradigm for Head and Neck Cancer Treatment Applying Artificial Intelligence. HNC-TACTIC.

Towards A Better Paradigm for Head and Neck Cancer Treatment Applying Artificial Intelligence: an International Cohort Study of Electronic Health Records. HNC-TACTIC.

To describe the demographics, clinical characteristics, and treatment of patients with nasopharynx, paranasal sinus, and salivary gland tumors.

To develop a predictive model based on dynamic risk stratification (DRS) for the risk of recurrence or disease progression following a primary curative treatment in HNSCC patients with early and locally advanced disease.

To develop a predictive model based on dynamic risk stratification (DRS) aimed at identifying patients' features that predict long-term survival after immunotherapy in recurrent and metastatic HNSCC patients

* To describe the demographics, clinical characteristics, and treatments
* To describe the patterns of follow-up
* Departments in charge
* Number of visits
* Imaging and anatomopathological tests
* Recurrence detected by physical examination
* To evaluate the impact of treatments on patients with locally advanced stages of the disease.
* Patients' early and late toxicity to the treatment, comparing between radiotherapy (cisplatin or cetuximab) vs surgery and post-operative radiotherapy (cisplatin)
* Healthcare resource utilization (HCRU), including medical visits, diagnostics, and hospitalizations.
* To compare OS in locally advanced HNSCC patients (including both HPV+ and HPV- oropharyngeal patients) treated with cisplati

* To describe the epidemiologic and clinical characteristics, and treatments
* To describe epidemiologic, clinical, and tumor characteristics of long-term survivors treated with immunotherapy and non-immunotherapy approaches.
* To describe the impact of introducing immunotherapy in recurrent or metastatic

HNSCC:

* Treatment outcome: OS
* HCRU, including medical visits, diagnostics, and hospitalizations.

Head and Neck Cancer International Group (HNCIG)

Savana Research

Oregon OCHIN patients that put their names on the reservation list for the Oregon Experiment and were randomly chosen to apply for insurance coverage via the Oregon Experiment

Oregon OCHIN patients that put their names on the reservation list for the Oregon Experiment and were not chosen to apply for insurance coverage via the Oregon Experiment

Random selection to apply for Medicaid coverage through Oregon Experiment drawings held between March and October 2008

Insurance Intervention

Jennifer E DeVoe, MD, DPhil

Heather Angier, MPH

This study examines the impact of a randomized insurance experiment on preventive services receipt and healthcare utilization in safety net patients using linked public insurance claims and safety net clinics' electronic health record (EHR) data.

In 2008, the Oregon Medicaid program used a "lottery" (Oregon Experiment) to allocate limited insurance resources to low income adults who did not categorically qualify for traditional Medicaid coverage. Thousands were randomly selected to apply for Medicaid; many others were not selected. This "natural policy experiment" is the first population-level randomization of insurance coverage since Rand's 1971-1982 experiment. We will use data from safety net clinics that share a common electronic health record (EHR) operated by OCHIN, a non-profit organization in Portland, Oregon. As one of the nation's largest linked community health center networks, the OCHIN EHR database records nearly all visits to safety net clinics in Oregon with one patient chart linked across \>100 community health centers. We will link Medicaid claims data and the OCHIN database to determine patients that had applied to participate in the Oregon Experiment, as well as to determine those that gained insurance via this experiment. We will test the hypothesis that public insurance coverage is associated with higher rates of receiving preventive care services and increased healthcare utilization among persons with a usual source of primary care. To that end, we will analyze preventive care and utilization patterns among the OCHIN clinic patients affected by Oregon's Medicaid Experiment. This study capitalizes on two unique opportunities: an EHR database linking \>100 community health centers, and the quasi-experimental design of Oregon's randomized Medicaid experiment.

The specific aims related to the Oregon Experiment are:

1) To compare post-intervention rates of preventive services receipt among OCHIN patients randomized to the Oregon Experiment group (intervention group), compared to those not in the coverage group (comparison group); and 3) To compare post-intervention rates of ambulatory healthcare utilization among OCHIN patients randomized to the Oregon Experiment group (intervention group), compared to those not in the coverage group (comparison group).

Assessing a Medicaid Randomized Insurance Experiment Within Community Clinics

Number of patients with receipt of the following preventive services in the post-period (each outcome assessed individually by whether person met age, gender, and diagnosis criteria (if applicable): body mass index, blood pressure, smoking, pap test, mammography, colorectal cancer screening, chlamydia screening, lipid test, flu vaccination, pneumococcal vaccination, HbA1c test, glucose test) using ICD-9-CM diagnosis and procedure codes, CPT and HCPCS codes, LOINC codes, medication codes, relevant code groupings and codes specific to the OCHIN EHR.

Number of visits in the post-period for the following services: total primary care office visits, total behavioral/mental health visits, labs, referrals, immunizations, imaging)

Kaiser Foundation Hospitals, Center for Health Research

OCHIN, Inc.

National Heart, Lung, and Blood Institute (NHLBI)

Oregon Health and Science University

Associate Professor

on-screen, point-of-care computer reminders on clinical practice.

No on-screen, point-of-care computer reminders on clinical practice.

computer reminders on clinical practice

Control group

Alessandro Passardi, MD

Implementing an evidence-based computerized decision support system linked to electronic health records to improve care for cancer patients.

The ONCO-CODES (Computerized DEcision Support in ONCOlogy) trial is a pragmatic, parallel group, randomized controlled study with 1:1 allocation ratio Study Duration 12 month Study Center(s) Single-center

Objectives:

The primary outcome of this trial is a process outcome. i.e. the rate at which the issues reported by the reminders are resolved (resolution rates).

Implementing an evidence-based computerized decision support system linked to electronic health records to improve care for cancer patients.

Short Title/Acronym ONCO-CODES Protocol Code IRST100.23

Rationale:

Computerized decision support systems (CDSSs) are computer programs that provide clinicians, staff, patients, or other individuals with person-specific, actionable recommendations or management options that are intelligently filtered or presented at appropriate times to enhance health and health care. CDSSs might be integrated with patient electronic health records (EHRs) and evidence-based knowledge. The Investigators designed a pragmatic randomized controlled trial to evaluate the effectiveness of patient-specific, point-of-care reminders generated by the Medilogy Decision Support System (MediDSS) on clinical practice and the quality of care in the Istituto Scientifico Romagnolo per lo Studio e la Cura dei Tumori (IRST) IRCCS. The Investigators hypothesize that MediDSS reminders can increase clinician adherence to guidelines and, eventually, improve the quality of care offered to hospitalized patient. The adoption of CDSSs is likely to increase across healthcare systems due to growing concerns about the quality of medical care and discrepancy between real and ideal practice, continuous calls for a meaningful use of health information technology, and the increasing use of and familiarity with advanced technology among new generations of physicians.

Study Design:

The ONCO-CODES (Computerized DEcision Support in ONCOlogy) trial is a pragmatic, parallel group, randomized controlled study with 1:1 allocation ratio Study Duration 12 month Study Center(s) Single-center

Objectives:

The primary outcome of this trial is a process outcome. i.e. the rate at which the issues reported by the reminders are resolved (resolution rates).

Number of Subjects:

The investigators calculated the sample size on the basis of the primary outcome. A sample of 1,704 reminders will be necessary to detect the difference between the two groups (power = 0.90; α =0.05, two-sided; 1:1 allocation). Since estimates for intracluster correlation are not available, Implementing an evidence-based computerized decision support system linked to electronic health records to improve care for cancer patients.

Diagnosis and Main Inclusion Criteria:

The investigators will include all the patients admitted to the facilities of the IRST IRCCS. There are no exclusion criteria

Statistical Methodology:

All analyses will follow the intention-to-treat principle: patients will be analyzed in the group to which they have been randomized. Descriptive statistics will be presented.

All statistical tests will be two-sided. The investigators will use the Stata software to perform all statistical analyses (Stata Corp., College Station, TX, USA).The investigators increased the required sample size (by 20%) to 2,046 reminders to account for clustering by patient.

Implementing an Evidence-based Computerized Decision Support System Linked to Electronic Health Records to Improve Care for Cancer Patients

Effect on Professional Practice and Health Care Outcomes of Computerized Evidence-based Information System and Clinical Decision Support: A Randomized Controlled Trial in Orthopaedics (GR-2009-1606736)

the rate at which the issues reported by the reminders are resolved

Ministry of Health, Italy

University of Milan

I.R.C.C.S Ospedale Galeazzi-Sant'Ambrogio

Catholic University of the Sacred Heart

Duodecim, Finnish Medical Association, Helsinki

Mario Negri Institute for Pharmacological Research

Ottawa Hospital Research Institute

Istituto Scientifico Romagnolo per lo Studio e la cura dei Tumori

Digestive System Diseases

Infections

Respiratory Tract (Lung and Bronchial) Diseases

Colorectal Neoplasms

COVID-19

Intestinal Neoplasms

Gastrointestinal Neoplasms

Digestive System Neoplasms

Gastrointestinal Diseases

Colonic Diseases

Intestinal Diseases

Rectal Diseases

Leadership, providers, and staff at the study team's affiliated health systems.

Patients who had a least one primary care visit during the past 24 months at the study team's affiliated health care systems.

The interview guide will consist of questions to elicit the patients thoughts about getting preventive healthcare during the COVID-19 pandemic, including perception of risk, barriers to getting healthcare, and information needed for decision making.

Semi-structured interviews-Patients

The interview guide will consist of questions to elicit thoughts from health system leadership, providers, and staff about implementing decision aids, provider notifications, and cancer risk assessments in their health center or healthcare system. The questions may be specific to colorectal cancer screening or more generally about other cancer screenings. The investigators may also ask questions about cancer screening initiatives their health center or healthcare system engaged in during the COVID-19 pandemic.

Semi-structured interviews-Health system

Karen K Schmidt, MSN

The rate of screening for colorectal cancer (CRC) in the U.S. remains low (under 65%), meaning that thousands of people die of colorectal cancer unnecessarily. Colorectal cancer screening tests range from more invasive and very sensitive for polyps and cancer (colonoscopy) to less invasive and less sensitive (e.g., fecal immunochemical testing (FIT)). Screening rates go up when patients consider all these tests, not just colonoscopy. Informing patients about their options for CRC screening could produce higher quality decisions, improve the match between patient preferences and tests performed, and increase uptake of CRC screening. Decision aids (DAs) are a promising tool for accomplishing this goal. Also, precision CRC prevention - providing information about an individual's specific risk for CRC - has great promise to increase uptake and improve decision making.

Unfortunately, the COVID-19 pandemic is causing severe challenges to providing CRC screening and other prevention services. Health systems are trying to adapt, but these efforts have only begun and are poorly understood. Moreover, patient perceptions of disease risk and risk from COVID-19 are unknown.

The study team will engage with the leadership, staff, and providers in the study team's partner healthcare systems, to identify facilitators and barriers to implementing patient decision aids and provider notifications as well as cancer risk assessment tools, for colorectal cancer screening, and for other evidence-based cancer screening during the COVID pandemic and, potentially, after the conclusion of the pandemic.

At the conclusion of the study, the investigators will have extensive information regarding how best to provide decision aids through an electronic health record (EHR) portal, with or without personalized information, and to deliver provider notifications, which can guide broader implementation.

The study will involve interviews with staff and providers at the study team's partner healthcare systems to identify facilitators and barriers to implementing decision aids and provider notifications for colorectal cancer screening.

Also, the investigators will interview patients to identify perceptions of prevention during the COVID-19 pandemic including risk perception and barriers to screening, perceptions of risk from both the pandemic and disease, and patient cancer screening and risk prevention behaviors engaged in or postponed during the pandemic and patient rationales for their decisions. This part of the study will suggest potentially promising approaches for providing prevention and disease management during the COVID-19 pandemic.

Facilitators and Barriers to Cancer Screening: Stakeholder Perspectives on Implementation

Semi-structured interviews with health system leadership, providers and staff conducted every 3-4 months

Semi-structured interviews with health system leadership, providers, and staff

Semi-structured interviews with primary care patients

Patient-Centered Outcomes Research Institute

Indiana University

Principal Investigator

Skin and Connective Tissue Diseases

Breast Neoplasms

Breast Diseases

Skin Diseases

alert from SEBASTIAN decision support system

SEBASTIAN is an example of a clinical decision support technology that supports the latest, service-based architectural approach to CDSS implementation. Developed at Duke University, SEBASTIAN is a clinical decision support Web service whose interface is now the basis of the HL7 Decision Support Service draft standard SEBASTIAN places a standardized interface in front of clinical decision support knowledge modules and makes only limited demands on how relevant patient data are collected or on how decision support inferences are communicated to end-users

SEBASTIAN Clinical Decision Support System (CDSS)

Ana M Gomez, MD

Damian A Borbolla, MD

Clinical decision support has been shown to improve the performance of screening tests; however, few studies have documented direct clinical benefit resulting from the increased screening promoted by clinical decision support systems.

The purpose of this study was to determine if a standards-based, sophisticated decision support system could not only promote additional breast cancer screening, but also detect significantly more breast cancer

Breast cancer is the most common female cancer. In the United States, the second most common cause of cancer death in women, and the main cause of death in women ages 45 to 55 years old. The U.S. Preventive Services Task Force recommends screening mammography, with or without clinical breast examination, every one to two years among women aged 50 to 69 years old.

Recent research has shown that health care delivered in industrialized nations often falls short of optimal, evidence based care. US adults receive only about half of recommended care. To address these deficiencies in care, health-care organizations are increasingly turning to clinical decision support systems. A clinical decision-support system is any computer program designed to help health-care professionals to make clinical decisions. In a sense, any computer system that deals with clinical data or knowledge is intended to provide decision support.

Examples include manual or computer based systems that attach care reminders to the charts of patients needing specific preventive care services and computerized physician order entry systems that provide patient-specific recommendations as part of the order entry process. Such systems have been shown to improve prescribing practices, reduce serious medication errors, enhance the delivery of preventive care services, and improve adherence to recommended care standards.

The aim of this study is to show the efficacy of a decision-support system as a strategy for improving the performance of the mammography care process and the detection of significantly more breast cancer.

Effectiveness of a Decision Support System in Improving the Diagnosis and Screening Rate of Breast Cancer

New Breast Neoplasms Diagnosis (Incident cases from biopsy reports). Breast Neoplasms are stored in the institutional Clinical Data Repository. The diagnosis are automatically codified using a terminology server that use SNOMED-CT as reference terminology

Number of patient with breast cancer screening due that received the order to perform the study (mammography). This information will be record through the clinical data repository every time that a provider order a screening test to each of the patients involve in the study

Duke University

Hospital Italiano de Buenos Aires

Urinary Tract, Sexual Organs, and Pregnancy Conditions

Carcinoma

Lung Neoplasms

Carcinoma, Hepatocellular

Urinary Bladder Neoplasms

Adenocarcinoma

Neoplasms, Glandular and Epithelial

Neoplasms by Histologic Type

Liver Neoplasms

Liver Diseases

This intervention will consist of two related, continuous steps over at least a 12-month period. In the first step, the investigators will query the VA's Corporate Data Warehouse (CDW, a repository of near real-time patient data from all VA medical centers) weekly to identify possible lost to follow-up events in a pre-specified time period and for a random sample of about half of the providers at the investigators' study sites. These identified patient charts will be reviewed by Facility Recipients/Cancer Trackers at each site who will then communicate patients truly found to be lost to follow-up to the appropriate provider/care team.

In the usual care group, providers will continue to use the existing notification system to receive abnormal test results in accordance with institutional norms, policies, and procedures. There are no formal patient-tracking programs currently at our study sites for all abnormal test results. The investigators will apply our computerized surveillance tools in the usual care arm only when the investigators are ready to conduct the final chart reviews on intervention patients and identify these patients in similar time periods as in the intervention arm. If persistent delays are found, the investigators will inform the patients' primary care providers.

The investigators will query the VA's database weekly to identify possible lost to follow-up events for a random sample of about half of the providers at our study sites. The queries will use the trigger criteria developed in our previous work for colorectal cancer, lung cancer, hepatocellular carcinoma, breast cancer, and bladder cancer. The list of trigger positive patients will be transmitted to a facility-level recipient who will either disseminate the information to existing facility individual cancer care coordinators/trackers or will review the charts of the "triggered" patients in order to determine whether these patients have been truly lost to follow-up or not. If patients are found to be lost to follow-up, the Facility Recipient or cancer care coordinator/tracker will communicate the need for follow-up to the PACT or primary care provider, using secure emails, phone calls, or in person, depending on which method of communication they deem most appropriate and effective.

Communication of Patients Lost to Follow-up to Providers

Hardeep Singh, MD MPH BS

Many missed and delayed cancer diagnoses result from breakdowns in communication and coordination of abnormal findings suspicious for cancer, which often first emerge in the primary care setting. Delays in the follow-up of abnormal test results persist despite the reliable delivery of test results through the electronic health record.

This intervention is the final study in a three-phase project that will develop and test an innovative automated surveillance intervention to improve timely diagnosis and follow-up of five common cancers in primary care practice.

The investigators hypothesize that the median time in days from diagnostic clue to follow-up action (e.g. time to colonoscopy examination after am abnormal colon-related test) will be significantly less in the intervention arm than in usual care. The investigators also hypothesize that the proportion of patients receiving appropriate and timely follow-up care will be significantly higher in the intervention arm than in usual care.

The CREATE Project encompasses three phases, the first and second of which do not contain interventions. The first phase of the project determines the effectiveness of computerized queries the investigators develop to accurately identify which patients are at risk for delays in cancer diagnosis. Patients the investigators identify will have abnormal test results or symptoms that have not been followed up by their providers. In Phase 2 of the study, the research team will use interviewing and other participatory techniques to determine the best way to convey information about such at-risk patients to providers in an automated fashion. In Phase 3 of the project, the investigators will evaluate the effects of an automated surveillance intervention on the timeliness of the diagnostic process of five cancers.

This project will improve communication and coordination of cancer-related diagnostic information to improve quality and safety.

Intervention Protocol for Automated Point-of-Care Surveillance of Outpatient Delays in Cancer Diagnosis

Automated Point-of-Care Surveillance of Outpatient Delays in Cancer Diagnosis

The investigators will conduct chart reviews of patients shown by our automated surveillance system to have not received appropriate follow-up care in both intervention and control groups at least 6 months after the first documentation of a diagnostic clue (e.g., initial abnormal chest X-ray). Chart review will be used to quantify time in days from documentation of the clinical clue to the time when follow-up action on that clue was initiated.

Study title	NCT ID	Conditions	Interventions	Eligibility	Organization
Towards A Better Paradigm for Head and Neck Cancer Treatment Applying Artificial Intelligence. HNC-TACTIC.	NCT05117775	Head and Neck C...	No intervention...	18 Years -	Savana Research
Assessing a Medicaid Randomized Insurance Experiment Within Community Clinics	NCT02355132	Cardiovascular ... Cancer	Insurance Inter...	19 Years - 64 Years	Oregon Health and Science University
Implementing an Evidence-based Computerized Decision Support System Linked to Electronic Health Records to Improve Care for Cancer Patients	NCT02645357	Medical Oncolog...	computer remind... Control group	18 Years -	Istituto Scientifico Romagnolo per lo Studio e la cura dei Tumori
Facilitators and Barriers to Cancer Screening: Stakeholder Perspectives on Implementation	NCT04683744	Colorectal Canc...	Semi-structured... Semi-structured...	18 Years -	Indiana University
Effectiveness of a Decision Support System in Improving the Diagnosis and Screening Rate of Breast Cancer	NCT01336257	Breast Cancer	SEBASTIAN Clini...	50 Years - 69 Years	Hospital Italiano de Buenos Aires
Implementing an Evidence-based Computerized Decision Support System Linked to Electronic Health Records to Improve Care for Cancer Patients	NCT02645357	Medical Oncolog...	computer remind... Control group	18 Years -	Istituto Scientifico Romagnolo per lo Studio e la cura dei Tumori
Towards A Better Paradigm for Head and Neck Cancer Treatment Applying Artificial Intelligence. HNC-TACTIC.	NCT05117775	Head and Neck C...	No intervention...	18 Years -	Savana Research
Intervention Protocol for Automated Point-of-Care Surveillance of Outpatient Delays in Cancer Diagnosis	NCT01710293	Lung Cancer Bladder Cancer Colorectal Canc... Hepatocellular ... Breast Cancer	Communication o...	21 Years -	VA Office of Research and Development
Facilitators and Barriers to Cancer Screening: Stakeholder Perspectives on Implementation	NCT04683744	Colorectal Canc...	Semi-structured... Semi-structured...	18 Years -	Indiana University

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