Neoplasms

Skin and Connective Tissue Diseases

All Conditions

Breast Neoplasms

CDSS (MedicBK) captures the patient-specific data required to determine appropriate care decisions from the EMR and then presents relevant data alongside treatment suggestions to clinicians. MedicBK uses algorithms for presenting treatment suggestions framed by guideline-based therapy and personalized personalized evidence-based therapy. MedicBK consists of three main blocks: database, network meta-analysis, and heterogeneity of treatment effects assessment.

Neoadjuvant Chemotherapy by CDSS (MedicBK)

General practice

Evgeny Pokushalov, Prof. MD PhD

Irina Ostanina, MD

This study compares two approaches of upfront chemotherapy in breast cancer.

A Decision Support System (DSS) is an information system that supports business, medical, educational or organisational decision-making activities. DSSs fuse human knowledge and technology to support and improve decision-making.

The area of precision oncology that supports the treatment of breast cancer has not only seen an increase in the availability of different treatment choices but also genomic tools to support the decision-making process. CDSSs have been available for use in clinical oncology practice for over a decade. However, there has been some criticism around the utility of CDSS in personalised medicine decision-making of breast cancer, with these systems often viewed as aids better suited to support the 'average' patient's requirements rather than personalised treatment for the individual patient.

The purpose of the current study is to evaluate an innovative CDSS (MedicBK) platform based on the comparison of multiple treatments simultaneously in making care decisions taking into account the individual characteristics of patients, i.e., based on personalized evidence-based medicine.

Neoadjuvant Chemotherapy in Breast Cancer

Optimizing Neoadjuvant Systemic Treatment for Breast Cancer by Clinical Decision Support System

To compare the efficacy of Neoadjuvant Chemotherapy in general practice and by CDSS

to describe the safety of the various regimens toxicity is compared between the two arms

To identify prognostic and predictive for pathological complete response after neoadjuvant treatment

Center for New Medical Technologies, Novosibirsk, Russia

I.M. Sechenov First Moscow State Medical University

Novosibirsk Oncology Center

Center of Personalized Medicine, Pirogova

Director for research and development

Urinary Tract, Sexual Organs, and Pregnancy Conditions

Behaviors and Mental Disorders

Prostatic Neoplasms

Aggression

Genital Neoplasms, Male

Urogenital Neoplasms

Genital Diseases

Genital Diseases, Male

Urogenital Diseases

Prostatic Diseases

Male Urogenital Diseases

Participants assigned to the control arm will receive usual care, including whatever information materials are provided to them by their urologist.

Participants assigned to the intervention will receive Decision Support Intervention in the form of a decision aid plus health coaching. The decision aid (delivered by internet and as a Portable Document Format (PDF) document) provides participants with a report on options and outcomes as described in the literature; along with more tailored risk information. The tailored risk information will include their estimated risk of harboring more aggressive prostate cancer based on their clinical/pathologic features (i.e., "My Clinical Risk"). The DSI was developed and piloted at UCSF according to the International Patient Decision Aid Standards (see http://ipdas.ohri.ca/) (IRS# 14-13332), and incorporates tailored risk models developed and validated.

A question list (QL) that includes areas of patient concern are created by the health coach for use by the patient and the urologist at their first consultation visit where they discuss the extent of their cancer and consider treatment options. The coach uses the Prostate Cancer SCOPED model to complete a "Prostate Cancer SCOPED Model Form". The SCOPED model uses concepts such as the situation, choices (treatment), objectives (personal goals and priorities), people (involved in supporting a treatment decision), evaluation and decisions (clarifying which choice is best and next steps).

Decision Support Intervention (DSI)

Peter R Carroll, MD, MPH

This clinical trial evaluates the use of novel decision support educational materials and services using health coaches. The study includes men newly diagnosed with low-risk prostate cancer. A 160 men will be recruited. Half of the men will receive a call from a health coach before their initial consultation visit with their urologist to review their treatment concerns and questions. The other half will receive usual care provided by the urologist, such as educational materials and services provided by the urologist.

A critical public health need exists for improved prognostic tools to distinguish aggressive from slow growing prostate cancer at diagnosis, and for better support systems to guide patients in decision-making regarding management options.

Decision support interventions that are tailored to specific clinical conditions are known to have increased patient self-efficacy, knowledge, question-asking, and satisfaction; and decreased decisional conflict, regret, anxiety, and distress.

To reduce the risk of over-treatment, our team has developed individual risk prediction models that we have now integrated into our decision support intervention (DSI).

Delivering such decision support intervention should increase patient knowledge and question-asking by the patient to their doctor.

The decision support intervention can be delivered by telephone and or the Internet.

This is a site-randomized, cluster-crossover clinical trial of a decision support intervention (DSI) vs. usual care, among men with low prognostic risk prostate cancer, to assess differences in informed decision making (i.e., knowledge), anxiety, and decision quality and self-efficacy.

Pioneering Advances in Care and Education (PACE)

Development, Validation, and Dissemination of an Integrated Risk Prediction Model and Decision Aid to Discern Aggressive Versus Indolent Prostate Cancer

De-identified individual participant data for all primary and secondary outcome measures will be made available through a formal data request

The DQI measures patient Knowledge, Concordance, and Decision process. For each fact about prostate cancer item, a correct response = 1 point. Missing responses=0 points. Total score is calculated for all patients who complete at least half of the items and scaled from 0-100%, with higher scores indicating greater knowledge. Patients rate their goals and concerns on an 11-point importance scale from 0 (not important at all) to 10 (extremely important). These questions + one question about patient's treatment preference can be used to calculate a concordance score. Patients are asked about whether they were offered a choice, how much pros and cons were discussed, and whether the health care provider asked for their preferences. Participants get 1 point for a response of "yes" / "a lot/some.", total points are summed, then divided by total number of items for a decision making process score from 0-100%. Higher scores indicate a more shared decision making process.

The MAX-PC is an 18-item questionnaire with a 4-point Likert-type scale asking how frequently certain "comments made by men about prostate cancer" were true of the respondent (Roth et al., 2003). Each item is scored from 0 to 4 with anchors ranging from "Not at all" to "Often." The scale can be scored in its entirety by summing all the items or summary scores can be grouped into Prostate Cancer Anxiety (11 items), Prostate-specific antigen (PSA) Anxiety (3 items), or Fear of Re-occurrence (4 items).

The DSE measures self-confidence or belief in one's ability to make informed decisions and participate in shared decision making with health professionals. It is a 11-item instrument with a five-point response scale ranging from 0 (not at all confident) to 4 (very confident).

CentraCare Adult & Pediatric Urology

Lancaster Urology

San Francisco Veterans Affairs Medical Center

Palo Alto Medical Foundation

University of California, San Francisco

Symptoms and General Pathology

Pain

Questionnaire completed using pen and paper

Questionnaire is transferred wireless to the physician's PC

CCADSS

Questionnaire completed and transferred to the physician using standard methods

pen-paper

Stein Kaasa, MD prof

This study aims to investigate computer based symptom assessment in an outpatient cancer population, and the use of a computer based decision support system to facilitate the diagnosis and treatment of cancer related pain.

Primary hypothesis is, that this approach improves pain control and pain management in an unselected group of cancer patients in an outpatient setting.

* Improvement of average pain last 24 hours by at least 1.5 points on a 0-11 scale
* Improvement of worst pain last 24 hours by at least 1.5 points on a 0-11 scale
* An alteration in the prescribing dose of opioids in equipotent opioid dosage

Secondary hypothesis is, that this system improves overall symptom control and symptom management in an unselected group of cancer patients in an outpatient setting.

The traditional way of symptom assessment is by the paper-and-pen method, which suffers from several limitations. The assessment items are not individually adjusted to each patient and his/her subjective symptoms, the collected data is rarely used in clinical practice, and decision-support for the physician is not possible.

Although the body of evidence is accumulating regarding the benefits of computerised symptom assessment in cancer patients, there is still insufficient knowledge of the impact of computerised assessment tools on the management of cancer pain and other cancer related symptoms.

The COMBAT study aims to investigate if a computer based assessment of cancer related symptoms, and a computerized decision support can improve treatment of pain and other symptoms in cancer patients.

This is an open, comparative study with a sequential design with two consecutive study periods, the non-intervention period and the intervention period.

The computer-based clinical decision support system will utilize the following data to generate one or several treatment options:

1. Data from self assessment of cancer related symptoms
2. Data from relevant variables reported by the physician
3. Revisited guidelines on treatment of cancer pain

Computer Based Assessment and Treatment

- The Brief Pain Inventory (BPI, a self-reported pain assessment tool aiming to quantify two aspects of cancer pain: Pain intensity and the functional disability as a result of cancer pain.

- The Alberta Breakthrough Pain Assessment Tool for cancer patients (ABPAT), a questionnaire developed in order to measure several dimensions of cancer-related breakthrough pain. It contains 15 questions, and was translated into Norwegian by our research group according to EORTC's rules for translation.

- The Edmonton Symptom Assessment System (ESAS), a validated standardized questionnaire consisting of 10 items, which evaluate physical and psychological symptoms as well as general wellbeing.

St. Olavs Hospital

Norwegian University of Science and Technology

Digestive System Diseases

Rare Diseases

Neoplasm Metastasis

Carcinoma

Carcinoma, Hepatocellular

Cholangiocarcinoma

Klatskin Tumor

Neoplasms, Glandular and Epithelial

Neoplasms by Histologic Type

Adenocarcinoma

Liver Neoplasms

Gastrointestinal Neoplasms

Digestive System Neoplasms

Gastrointestinal Diseases

Liver Diseases

Bile Duct Cancer

Gallbladder Cancer

Intrahepatic Cholangiocarcinoma

Fibrolamellar Carcinoma

Participants' cases are discussed in tumor conference without the use of ADBoard, according to conventional practice.

Participants' cases will be evaluated by ADBoard directly before they are discussed in the tumor conference.

Utilizing the clinical decision support system 'ADBoard' for therapy selection in participants with hepatobiliary tumors

ADBoard

Felix Krenzien

Christian Benzing

The goal of this observational study is to compare the recommendations of the artificial intelligence clinical decision support system 'ADBoard', with the recommendations of physicians by tumor conferences in patients with hepatobiliary tumors. The main questions it aims to answer are:

Can ADBoard achieve a high level of similar recommendations as physicians' tumor conferences? Can ADBoard consider a more complete set of patient-related data than in physicians' tumor conferences? Can ADBoard reduce the time between the first time the patient is discussed at the tumor conference and the start of the recommended treatment plan? Participants will have their hepatobiliary tumor treatments determined by either tumor conference with ADBoard, or tumor conference without ADBoard.

The ADBoard study is a monocentric, prospective, parallel randomized controlled trial with a non-inferiority framework. Participants will be randomized 1:1 into one of two groups: either a) tumor conference with ADBoard, or b) tumor conference without ADBoard.

Therapeutic Assistance and Decision-making Algorithms in Hepatobiliary Tumor Boards

Evaluation of the Trustworthiness of the Application of Artificial Intelligence and Decision Support Systems for the Creation of Tumor Conference Protocols

Examples of possible treatment recommendations are liver resection (curative), regimen of chemotherapy, immunotherapy, radiotherapy, transarterial chemoembolisation, diagnostics, best-supportive care, follow-up by re-presentation.

The interrater reliability of the recommendations of ADBoard and the tumor conferences with regard to their agreement will be measured.

The intrarater reliability will be measured by testing all participant cases several times by the ADBoard according to the required sample size with sufficient statistical power (test-retest). Interrater and intrarater reliability will be evaluated descriptively (percentage of agreement, contingency tables), and finally the Cohen-Kappa value will be assessed.

For each type of tumor examined, a series of relevant parameters is defined by the medical staff. The objective is achieved if complete documentation, defined as documentation status 'present' or 'detected as missing', is present in more than or equal to 75% of all ADBoard-assisted decisions.

The System Causability Scale will be reviewed by 3 specialist physicians. The score ranges from 0 to 1. A higher score means greater suitability of the user interface, explanation, or explanation process itself for the intended purpose of tumor conference treatment recommendations.

The time between the initial presentation of the case to the tumor conference and the time of recommendation implementation will be recorded.

Study title	NCT ID	Conditions	Interventions	Eligibility	Organization
Neoadjuvant Chemotherapy in Breast Cancer	NCT04802941	Decision Suppor...	Neoadjuvant Che... General practic...	18 Years -	Center of Personalized Medicine, Pirogova
Pioneering Advances in Care and Education (PACE)	NCT03397160	Decision Suppor... Prostate Cancer	Decision Suppor...	18 Years -	University of California, San Francisco
Computer Based Assessment and Treatment	NCT01795157	Neoplasms Pain	CCADSS pen-paper	18 Years -	Norwegian University of Science and Technology
Computer Based Assessment and Treatment	NCT01795157	Neoplasms Pain	CCADSS pen-paper	18 Years -	Norwegian University of Science and Technology
Therapeutic Assistance and Decision-making Algorithms in Hepatobiliary Tumor Boards	NCT05681949	Hepatocellular ... Fibrolamellar H... Cholangiocarcin... Intrahepatic Ch... Metastasis to L... Gallbladder Car...	ADBoard	18 Years -	Charite University, Berlin, Germany
Therapeutic Assistance and Decision-making Algorithms in Hepatobiliary Tumor Boards	NCT05681949	Hepatocellular ... Fibrolamellar H... Cholangiocarcin... Intrahepatic Ch... Metastasis to L... Gallbladder Car...	ADBoard	18 Years -	Charite University, Berlin, Germany

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