Neoplasms

Urinary Tract, Sexual Organs, and Pregnancy Conditions

All Conditions

Skin and Connective Tissue Diseases

Prostatic Neoplasms

Breast Neoplasms

Uterine Cervical Neoplasms

Aims 1a and 1b: The participants include all unique patients seen at the 12 study practices (approximately 170,000 patients over the past two years).

Aims 2 and 3: The clinicians in this analysis will include 15 participants recruited from the approximately 100 clinicians at the 12 practices

We will present clinicians with information about their patients' communities

Community vital signs

Alex Krist, M.D.

Social determinants of health (e.g. the income, education, and environment of patients) may exert greater influence on health outcomes than traditional clinical factors (e.g. lab results, diagnoses, and family history). Calls for integrating primary care and public health are therefore increasing, but merging these domains of care is logistically difficult. Research is lacking on the incremental benefit of adding public health data at the practice level-- in improving either health outcomes or care delivery. This proof of concept pilot will merge data from electronic health records (EHRs) with community vital signs, a set of metrics that describes key community resources that affect health. The investigators will identify resource poor communities, or cold spots, based on four variables (education, poverty, life expectancy, and access to healthy foods) at the census tract level - referred to as a community vital sign. The hypothesis is that patients coming from cold spots are more likely to have worse health outcomes and that clinicians will deliver better care if they know a patient's community context and his/her specific social needs. This study will involve 12 primary care practices in Northern Virginia that care for more than 170,000 patients. Patient addresses will be geocoded for each practice and determine which patients reside in cold spots for each community vital sign. The variation for each community vital sign for each practice's patients will be calculated and a bivariate and regression analyses will be used to determine whether coming from a cold spot is associated with worse clinical quality metrics. 15 clinicians will be alerted when they see a patient from a cold spot, patients will complete a social needs survey, and clinicians will prospectively document through surveys whether such knowledge affects interpersonal interactions (such as time spent with patients and the use of clearer language) or clinical management (such as referrals to care coordination or community resources). By pragmatically integrating community vital signs into care, this innovative proposal will seek to understand which community data clinicians value, how these data might influence care, and how best to incorporate these data into clinical and population care.

The investigators will integrate community vital signs into clinical data, identify cold spots, and determine prospectively how clinicians use these community data at the point of care and for populations of patients. The investigators hypothesize that (1) the patients in a primary care practice live in a range of communities with different social determinants of health, including cold spots that lack health-promoting resources, (2) community vital signs are associated with patient health outcomes (e.g., being up-to-date with preventive care, control of chronic conditions, morbidity), and (3) knowing that a patient resides in a cold spot will help clinicians improve patient care.

Aim 1a: Determine community vital sign cold spots in the catchment area of 12 primary care practices.

Aim 1b: Determine whether patients residing in cold spots experience worse outcomes, such as receiving inadequate care, worse chronic disease control, or higher morbidity.

Aim 2: Determine whether knowing a patient's community vital signs and social needs at the point of care will change clinical management; and explore how practices could use community vital signs for population care.

Aim 3: Identify other community vital signs that practices would find useful for managing patients.

Understanding the Value of Community Vital Signs in Primary Care

Likelihood patient living in a cold spot received recommended cancer screening and cardiovascular preventive care based on National Committee for Quality Assurance (NCQA) quality measures.

Likelihood a patient living in a cold spot has a higher Adjusted Clinical Group (ACG) risk score than patient not living in a cold spot.

Physician report on whether they change care in response to knowing a patient lives in a cold spot.

Virginia Commonwealth University

Behaviors and Mental Disorders

Respiratory Tract (Lung and Bronchial) Diseases

Ear, Nose, and Throat Diseases

Anxiety Disorders

Laryngeal Neoplasms

Breast Diseases

Skin Diseases

Genital Neoplasms, Male

Urogenital Neoplasms

Genital Diseases

Genital Diseases, Male

Urogenital Diseases

Prostatic Diseases

Male Urogenital Diseases

Otorhinolaryngologic Neoplasms

Head and Neck Neoplasms

Laryngeal Diseases

Respiratory Tract Diseases

Respiratory Tract Neoplasms

Otorhinolaryngologic Diseases

Uterine Neoplasms

Genital Neoplasms, Female

Uterine Cervical Diseases

Uterine Diseases

Genital Diseases, Female

Female Urogenital Diseases

Pregnancy Complications

Female Urogenital Diseases and Pregnancy Complications

Issue of information booklet

Ray Jones, PhD

Objective: To explore the hypothesis that different methods of selecting and printing information for cancer patients could improve emotional support by affecting interaction with others, and so lead to improved psychological wellbeing.

Design: Randomised trial with 8 groups (three factors, 2X2X2). Data collected at recruitment and three month follow-up.

Participants: 400 patients starting radiotherapy and their 'confidant' (the person in who they confide).

Interventions: Printed booklets.

1. Half had 'general' CancerBACUP information for that cancer; half had 'personal' information from the medical record plus selected general information;
2. Half chose information by 'interacting' with the computer; half had a larger volume of material in booklets that were produced 'automatically'.
3. Half had additional 'anxiety management advice'.

Outcomes: Patients' views; use of booklet with others; change in reported social support; change in anxiety and depression.

Aim:

* Based on a randomised trial the researchers aimed to model use of leaflets in the home as a function of three factors:

  1. whether patients chose the information themselves or if it was chosen for them;
  2. whether it included personal information from their own medical record or not;
  3. whether it included anxiety management advice or not.
* The researchers aimed to model patient and confidant's anxiety at three months as a function of these three factors and patients' use of leaflets in the home.

Patient Population: Beatson Oncology Centre (BOC). Patients thought to be eligible were to be identified from outpatient appointment diaries over fifteen months. Medical records were to be checked for all those initially identified to ensure that patients have breast, gynaecological, prostate or bladder or testicular, or selected cancers of the head or neck. Patients excluded were to be those:

* receiving palliative treatment,
* with no knowledge or understanding of their diagnosis (very few),
* with visual or mental handicap,
* with severe pain or symptoms causing distress,
* medical or nursing students,
* taking part in other trials with possibly confounding interventions, and
* those not giving informed consent.

Sample size: The researchers aimed to recruit 400 patients. This was to give 80% power to find a difference between 40% and 27% who have improved anxiety between (eg) 200 in personalised group and 200 in general group.

Recruitment: Eligible patients were to be sent a letter explaining the study and advising them that a researcher will approach them in a visit to the BOC soon after, to seek participation in the study. Patients were not to be recruited at their bad news consultation but as soon as possible thereafter and at any point in their course of contact and treatment at the Beatson. At recruitment the researcher was to further explain the study and seek written consent. If the patient consented the researcher was to arrange a time for the intervention and give the patient a questionnaire to take home to complete themselves and a questionnaire for their 'confidant'.

Baseline data: Patients were to be asked to bring the completed questionnaire with them to the intervention consultation about one week later. The questionnaire included the following sections:

1. previous computer and information use, perceived knowledge, significant others in providing emotional support, family and living arrangements,
2. a Hospital Anxiety and Depression Scale (HADS),
3. a Miller Coping Style questionnaire,
4. Helgeson's measures of social support and negative interactions.

Completion of this questionnaire should have taken about half an hour at home.

Confidants: The researchers aimed to recruit 400 'confidants', one for each patient (but estimated that it were more likely to recruit (say) 60% of 400. Patients were to be asked if they were prepared to pass on the baseline questionnaire to a person selected by them as the person they are most likely to use to seek emotional support. This may be a husband, wife, partner, but may also be a daughter, son, or friend. The confidant questionnaire has sections:

1. demographics and relationship to patient, newspaper read, frequency of discussion with patient, perceived knowledge
2. HADS,
3. Miller coping style.

The researchers did not know what proportion of patients would be prepared to pass on the questionnaire or what response rate would be received from confidants. Rees in her work with daughters of women with breast cancer found that 93% of patients passed the questionnaires on and 77% of daughters responded. If we got an overall 60% response (ie 240 before and after questionnaires) this would give us 80% power to find a difference between 40% and 23% who had improved anxiety between (eg) 120 in personalised group vs 120 in general group. The confidant population would be less than the patient population. Patients will not be excluded if there is no corresponding 'confidant'.

Randomisation for the three factors: After recruitment the RA was to randomise for each of the three binary factors (2X2X2 factorial design):

A. Interactive v Automatic;

B. Medical Record v General;

C. Anxiety reducing advice v no advice.

Patients were to randomised to the eight sub-groups in blocks of 32 and separately by breast vs rest. The eight (2X2X2) subgroups, each receive different styles of leaflet. These are detailed further below.

Baseline Data: Collected from case notes was to be severity, length of onset of cancer, treatments so far (surgery, radiotherapy, chemotherapy), date of birth, gender, address, deprivation category from postcode for all patients, and a problem and treatment list for patients in the 'medical record' groups.

Intervention: The delay between recruitment and intervention was to allow patients time to think about their participation and withdraw if desired. At the intervention, patients were to produce a report about their cancer. Patients in the 'automatic group' would not need to use the computer.

* Interactive vs automatic. Those in the 'interactive' groups were to choose from menus, see information on the screen, and press an 'add to report' button on information screens for that information which they want to include in their report. Patients' choices were to be recorded by the computer.
* Those in the medical records group were to have personal information included based on their medical record. They were to either have information automatically selected and formatted by the computer, or to see it on screen and choose what they want to include.
* Lastly, half the patients were to have an additional section offering advice for the reduction of anxiety. This section was written based on previous work in cognitive behavioural therapy for anxiety.

All reports were to include colour photos (for example of treatment rooms, staff), and diagrams (for example, anatomical) of good quality printing etc. The patient were to be given the report to take away.

Three month follow-up: At three months patients were to be sent a follow-up questionnaire with the following sections:

1. questions about their satisfaction with the information received, their use of their reports, their interactions with their family and friends, and the influence of the reports on these interactions, their perceived knowledge.
2. HADS,
3. Helgeson's social support questionnaires.

Significant others will be sent a questionnaire which has sections on:

1. questions about their satisfaction with the information they have received,
2. HADS

Measures of Outcome:

* Change in anxiety (for patient or confidant) between intervention and 3 month follow-up (HADS)
* Change in social support (for patient) between intervention and 3 months (Helgeson)
* Patient satisfaction with information score at 3 month follow-up
* Confidant satisfaction with information score at 3 month follow-up
* Whether patient had read report, shown it or discussed it with the named confidant, other family members or friends and whether the discussion was aided by use of the report.
* What patients liked and disliked about the reports.
* Amongst the interactive group, choices made between presentation styles and how well these would correspond to automatic computer choices.
* Patient and staff time, and hence costs, needed for routine delivery of each intervention will be modelled on a variety of assumptions.

Analysis: The researchers were to produce a CONSORT patient flow diagram and compare losses to follow-up by the three interventions (A,B,C). Analysis of outcomes was to be for those patients completing both intervention and follow-up. The researchers will focus on the following:

* Comparison of outcomes 1-4 by interventions A, B, and C while controlling for age, gender, cancer site, time since diagnosis, severity, coping style, using analysis of variance (factorial design) and multiple logistic regression.
* Comparison of outcomes 5 and 6 by interventions A,B, and C (qualitative).
* Modelling of anxiety (outcomes 1-2) on use of report at home (outcome 4), perceived social support, and interventions A,B,C.
* Frequencies of choices (outcome 7) and comparison with automatic selection, both statistically and through qualitative description of the differences.
* Comparison of patient and NHS costs (outcome 8) will be made between the methods used in this trial and other methods of providing information.
* Comparison of anxiety change in patient and significant other.
* Analyses will be on those completing follow-up. No substitution of data will be carried out for those randomised but not completing follow-up. The researchers will thus be able to make provisional conclusions on how the different types of information provision influence interaction, anxiety and patient satisfaction, and on the correlation between the use of information with family, and anxiety; The researchers will have information on patient selection to inform the better development of automatic systems, and costs to inform practical deployment.

Investigating the Impact of Tailored Reports on Anxiety Amongst Cancer Patients and Their Confidants

Investigating the Impact of Tailored Reports on Anxiety

University of Plymouth

Symptoms and General Pathology

Rare Diseases

Chronic Disease

Chronic Graft Versus Host Disease

The randomization is at the level of attending healthcare professional who identifies patients for recruitment. For each participant in the intervention arm, a secure space (called a Patient Loop) is created in the online clinical communication system. A Patient Loop can be accessed by the patient, their caregiver, and the healthcare providers who have permission to do so through an algorithm of invitation, authentication and careful partitioning. In each Patient Loop, team members can post messages that can be read and responded to by the entire team. Each Patient Loop consists of a patient, their caregiver and at least two healthcare professionals.

Loop is a secure online communication system centered on the patient that assembles the patient's actual healthcare team for ongoing collaborative care. The patient and caregiver are integral members of the team. Loop is cross-organizational, cross-setting and interprofessional. It is for ongoing, interactive, contextual, team-based communication. Loop is explicitly for asynchronous communication, not instant messaging. The stream of messages is stored and can be sorted for ease of viewing. The tool was developed with user-centred design and requires no prior training.

Loop

Amna Husain, MD MPH

This project will pilot and test a new online communication tool, Loop, developed within a research framework with participatory and user-centred design. This pilot trial focuses on advanced cancer as an example of complex care. Cancer care involves many healthcare providers, spanning hospital to home. There is no organized way for them to communicate. Loop is a practical tool for ongoing collaboration in the patient's actual team of care that engages patients. The study will answer the questions: does Loop improve communication efficiency, engage patients and family physicians, and show early benefits in quality and health care costs?

My Team of Care: a Pilot Randomized Controlled Trial of an Online Communication Tool for Collaborative Care

My Team of Care (MyTOC) Trial: a Pilot Randomized Controlled Trial of an Online Communication Tool (Loop) for Collaborative Care in Complex Patients

Healthcare providers will be enrolled in the study for between 3 months and 1 year

A 12 item questionnaire measuring patient-reported quality of care

An 8 item questionnaire measuring patient-perceived continuity and coordination of care

Measure of ambulatory, home-based and acute care health services utilization, and associated public and private costs

A 10 item questionnaire measuring symptom intensity

Mount Sinai Hospital, Canada

Infections

Lung Neoplasms

COVID-19

Communicable Diseases

Tuberculosis

Tuberculosis, Pulmonary

Mycobacterium Infections

Bacterial Infections

Gram-Positive Bacterial Infections

Mycoses

Bacterial Infections and Mycoses

Disease Attributes

Respiratory Tract Infections

Lung Diseases

Respiratory System Agents

All Drugs and Chemicals

Participants with lung cancer/pulmonary nodules

wei geng, Phd

Yang Jin, MD

Yang Jin, Professor

To improve accurate diagnosis and treatment of common malignant tumors and major infectious diseases in the respiratory system, we aim to establish a large medical database that includes standardized and structured clinical diagnosis and treatment information such as electronic medical records, image features, pathological features, and multi-omics information, and to develop a multi-modal data fusion-based technology system for individualized intelligent pathological diagnosis and therapeutic effect prediction using artificial intelligence technology.

The main aims are as follows:

1. To establish a medical big data platform for multi-modal information fusion of common tumors and major infectious diseases (lung cancer/pulmonary nodules, tuberculosis, and COVID-19) based on the existing pathological image features and clinical multi-omics information database: The medical big data platform supports the acquisition of the patient's clinical electronic medical records (including routine clinical detection), full view digital section of pathological image data, medical imaging (CT, MRI, ultrasound, nuclear medicine, etc.), multiple omics data (genome, transcriptome, and metabolome, proteomics) omics data, etiology, pathology, and associated graphic data reports and multimodal medical treatment data. We aim to realize the storage, sharing, fusion computing, privacy protection, and security supervision of multi-modal and cross-scale biomedical big data. Our work will open up key business processes and links across regions, across hospitals, between different terminals, between hospitals and doctors, and between departments, so as to promote continuous data accumulation and knowledge precipitation in hospitals and promote medical collaboration.
2. To create a multimodal information fusion database with pathologic features, imaging features, multi-omics (pathologic, genomic, transcriptome, metabolome, proteomics, etc.), and clinical information of patients at different stages of lung cancer/pulmonary nodules, tuberculosis, and COVID-19. The database scale includes multimodal data of at least 600 lung cancer/pulmonary nodules, 200 tuberculosis, and 200 COVID-19 patients. Moreover, there will be more than 10 biomarkers significantly related to the diagnosis and treatment of patients with lung cancer/pulmonary nodules, tuberculosis and COVID-19 were excavated through association analysis, providing parameters for artificial intelligence model construction.
3. We will make use of artificial intelligence technology to create the multi-modal medical big data cross-analysis technology and the above disease individualized accurate diagnosis and curative effect prediction models. In order to solve the three key problems of multi-modal data fusion mining, such as unbalanced, small sample size, and poor interpretability, we will establish an ARTIFICIAL intelligence recognition algorithm for image images and pathological images, and use image processing and deep learning technologies to mine multi-level depth visual features of image data and pathological data. In addition, we will use bioinformatics analysis algorithms to conduct molecular network mining and functional analysis of molecular markers at the level of multiple omics technologies (pathologic, genomic, transcriptome, metabolome, proteome, etc.).

Development of an Artificial Intelligence System for Intelligent Pathological Diagnosis and Therapeutic Effect Prediction Based on Multimodal Data Fusion of Common Tumors and Major Infectious Diseases in the Respiratory System Using Deep Learning Technology.

Research and Development of an Artificial Intelligence Technology System for Digital Pathological Diagnosis and Therapeutic Effect Prediction Based on Multimodal Data Fusion of Common Tumors and Major Infectious Diseases in the Respiratory System Using Deep Learning Technology.

The outcome of clinical diagnosis of patients with lung cancer/pulmonary nodular (Benign/Malignant nodule).

① Benign nodule

② Malignant neoplasm/nodule: squamous cell carcinoma, adenocarcinoma, small cell carcinoma, and large cell carcinoma.

The outcome of clinical diagnosis of patients with pulmonary tuberculosis (Positive/Negative).

The outcome of clinical diagnosis of patients with COVID-19 (Positive/Negative).

The treatment response of anti-cancer therapy at first evaluation in patients with lung cancer/pulmonary nodules follows The Response Evaluation Criteria In Solid Tumors (RECIST version 1.1) from the World Health Organization (WHO). The evaluation index is as follows.

CR (complete response): Disappearance of all target lesions and reduction in the short axis measurement of all pathologic lymph nodes to ≤10 mm.

PR (partial response): 30% decrease in the sum of the longest diameter of the target lesions compared with baseline.

PD (progressive disease):≥20% increase of at least 5 mm in the sum of the longest diameter of the target lesions compared with the smallest sum of the longest diameter recorded OR The appearance of new lesions, including those detected by FDG-PET (fludeoxyglucose positron emission tomography).

SD (stable disease): Neither PR nor PD.

Treatment response of anti-inflammation and antiviral therapy at first evaluation in patients with COVID-19 (effective/ineffective treatment).

effective treatment： Improved total time to recovery, resolution of fever, cough remission, and pneumonia severity.

ineffective treatment： The above conditions have not improved or patients go die.

Treatment cure： patients with bacteriologically confirmed TB at the beginning of treatment who were smear- or culture-negative in the last month of treatment and on at least one previous occasion.

Treatment completer: patients who completed treatment without evidence of failure but with no record to show that sputum smear or culture results in the last month of treatment and on at least one previous occasion were negative.

Treatment success: The sum of cured and treatment completed.

Treatment failure: patients whose sputum smear or culture is positive at month 5 or later during treatment.

Treatment relapse: Patients who were declared cured or treatment completed at the end of their most recent course of TB treatment, and are now diagnosed with a recurrent episode of TB. This can be either a true relapse or a new episode of TB caused by reinfection.

Patient died.

The time interval between the date of treatment initiation and disease progression (Months) of patients with lung cancer/pulmonary nodules.

The time interval between the date of diagnosis and death (Months) of patients with lung cancer/pulmonary nodules.

Whole-genome sequencing of blood samples before and after treatment in patients with lung cancer/pulmonary nodular, tuberculosis, and COVID-19. Whole-genome sequencing is mainly used to find single nucleotide polymorphisms (SNPs), copy number variations, and insertions/deletions.

Whole-genome sequencing of tissue samples after surgery in patients with lung cancer/pulmonary nodular and tuberculosis. Whole-genome sequencing is mainly used to find single nucleotide polymorphisms (SNPs), copy number variations, and insertions/deletions.

Whole-genome sequencing of exhaled air condensate samples before and after treatment in patients with lung cancer/pulmonary nodular, tuberculosis, and COVID-19. Whole-genome sequencing is mainly used to find single nucleotide polymorphisms (SNPs), copy number variations, and insertions/deletions.

Whole-genome sequencing of urine specimens before and after treatment in patients with lung cancer/pulmonary nodular, tuberculosis, and COVID-19. Whole-genome sequencing is mainly used to find single nucleotide polymorphisms (SNPs), copy number variations, and insertions/deletions.

Transcriptome sequencing of blood samples before and after treatment in patients with lung cancer/pulmonary nodular, tuberculosis, and COVID-19. The collection of all transcripts, including messenger RNA, ribosomal RNA, transport RNA, and non-coding RNA.

Transcriptome sequencing of tissue samples after surgery in patients with lung cancer/pulmonary nodular and tuberculosis. The collection of all transcripts, including messenger RNA, ribosomal RNA, transport RNA, and non-coding RNA.

Transcriptome sequencing of exhaled air condensate specimens before and after treatment in patients with lung cancer/pulmonary nodular, tuberculosis, and COVID-19. The collection of all transcripts, including messenger RNA, ribosomal RNA, transport RNA, and non-coding RNA.

Transcriptome sequencing of urine specimens before and after treatment in patients with lung cancer/pulmonary nodular, tuberculosis, and COVID-19. The collection of all transcripts, including messenger RNA, ribosomal RNA, transport RNA, and non-coding RNA.

Metabolomics of blood specimens before and after treatment in patients with lung cancer/pulmonary nodular, tuberculosis, and COVID-19. Non-target metabolites are generally analyzed qualitatively and quantitatively based on LC-MS technology for metabolites in samples, and identified by matching primary and secondary information with local self-built databases and commercial standard databases.

Metabolomics of tissue samples after surgery in patients with lung cancer/pulmonary nodular and tuberculosis. Non-target metabolites are generally analyzed qualitatively and quantitatively based on LC-MS technology for metabolites in samples, and identified by matching primary and secondary information with local self-built databases and commercial standard databases.

Metabolomics of exhaled air condensate specimens before and after treatment in patients with lung cancer/pulmonary nodular, tuberculosis, and COVID-19. Non-target metabolites are generally analyzed qualitatively and quantitatively based on LC-MS technology for metabolites in samples, and identified by matching primary and secondary information with local self-built databases and commercial standard databases.

Metabolomics of urine specimens before and after treatment in patients with lung cancer/pulmonary nodular, tuberculosis, and COVID-19. Non-target metabolites are generally analyzed qualitatively and quantitatively based on LC-MS technology for metabolites in samples, and identified by matching primary and secondary information with local self-built databases and commercial standard databases.

Proteomics of blood specimens before and after treatment in patients with lung cancer/pulmonary nodular, tuberculosis, and COVID-19. Unlabeled proteomics technology based on the timsTOF Pro ion mobility platform for differential quantitative proteomics analysis using data-dependent acquisition - Synchronous cumulative continuous fragmentation (ddaPASEF) scan mode.

Proteomicstissue samples after surgery in patients with lung cancer/pulmonary nodular and tuberculosis. Unlabeled proteomics technology based on the timsTOF Pro ion mobility platform for differential quantitative proteomics analysis using data-dependent acquisition - Synchronous cumulative continuous fragmentation (ddaPASEF) scan mode.

Proteomics of exhaled air condensate specimens before and after treatment in patients with lung cancer/pulmonary nodular, tuberculosis, and COVID-19. Unlabeled proteomics technology based on the timsTOF Pro ion mobility platform for differential quantitative proteomics analysis using data-dependent acquisition - Synchronous cumulative continuous fragmentation (ddaPASEF) scan mode.

Proteomics of urine specimens before and after treatment in patients with lung cancer/pulmonary nodular, tuberculosis, and COVID-19. Unlabeled proteomics technology based on the timsTOF Pro ion mobility platform for differential quantitative proteomics analysis using data-dependent acquisition - Synchronous cumulative continuous fragmentation (ddaPASEF) scan mode.

forced expiratory volume in one second (FEV1) for lung volume

carbon monoxide diffusion capacity (DLCO) for pulmonary diffusion function.

St. George's Respiratory Questionnaire total score(0-3989.4), St. George's Respiratory Questionnaire symptoms score(0-662.5); St. George's Respiratory Questionnaire impacts score(0-2117.8); St. George's Respiratory Questionnaire activity score(0-1209.1). The higher the score, the worse the lung.

Serum tumor markers including Carcinoembryonic Antigen (ug/L), Cytokeratin 19 fragment , Squamous Cell Carcinoma Antigen(ug/L), Nervous specific enolase (U/mL), Tissue Polypeptide Specific Antigen(ug/L), Cancer antigen 125 (U/mL), Cancer antigen 15-3 (U/mL), Bombesin (U/mL), The stomach secrete ty (U/mL), β2-microglobulin (U/mL).

Etiological detection including Mycoplasma, Chlamydia, Viruses, Bacteria (especially Mycobacterium tuberculosis), and Fungi. (Positive/Negative)

Wuhan Union Hospital, China

Department of Respiratory and Critical Care Medicine

Digestive System Diseases

Colorectal Neoplasms

Intestinal Neoplasms

Gastrointestinal Neoplasms

Digestive System Neoplasms

Gastrointestinal Diseases

Colonic Diseases

Intestinal Diseases

Rectal Diseases

The TOP-CARE intervention will be based on a medical informatics platform that:

1. Identifies all patients eligible for any of the three cancer screening programs
2. Links patients with a specific clinician
3. Offers a visit-independent method for clinicians to review panels of their eligible patients
4. For patients due for one or more cancer screenings, clinicians will access a web-based informatics tool to:

   1. Screen their panel based upon risk
   2. Defer patients, document exclusions, and update the EHR
   3. Order a screening test with patient information material based upon the patient's risk profile and automatically initiate the process of:

      1. Informing the patient by letter of the need to schedule a test, educating the patient with respect to the benefits of cancer screening, and properly documenting the transaction in the patient's EHR, or
      2. Referral to a patient navigator for patients most likely to benefit from this more intensive approach

In augmented standard care control practices, we will implement a system that includes: 1) a population-based perspective to identify all eligible patients overdue for screening, 2) an automated, centralized process to contact selected patients by letter, 3) a result management system that automatically tracks test scheduling and completion, 4) a web-based, easily accessible tool allowing practice personnel to contact patients not completing testing, and 5) use of patient navigators for high risk patients not responding to initial outreach. In the control arm, the process of escalating the reminder intervention from a letter, to contact by phone call, to a patient navigator, will occur in a standard algorithmic fashion without provider input.

We will be evaluating the system in practices where providers are involved in determining to contact a patient, including the most appropriate outreach method, compared with practices where the system is fully automated without provider input.

TopCare: Use of provider knowledge vs. automated system

Steven J Atlas, MD, MPH

Although there is considerable evidence that current health IT can improve certain elements of care, the most effective and efficient implementation of health IT systems for primary care population management are not currently known. Indeed, while many systems currently take a "case-management" approach to identify and address clinical care issues for high risk patients, no systems to our knowledge apply a risk-based approach that accounts both for adverse clinical outcome risk (e.g. breast cancer in a woman who has not had indicated screening for 4 years) and for clinical process risk (e.g. the likelihood that a specific patient will ignore a reminder letter and would therefore benefit from direct phone or in person contact). The investigators propose to directly test the hypothesis that implementing a health IT platform that 1) provides novel risk-based decision support using data derived from the electronic health record (EHR) and 2) leverages each clinician's unique knowledge of his or her patient panel will result in more effective and more efficient population-based primary care. The investigators will test this hypothesis in a practice-randomized clinical trial of preventive cancer screening within our primary care Practice-Based Research Network (PBRN).

In prior NIH-funded research, the investigators have demonstrated the efficacy of an IT-based population management system to improve breast cancer screening (NCI R21 CA121908). The investigators will expand our current IT platform from this single function (breast cancer screening) to a package of cancer prevention actions (breast, cervical, and colorectal cancer screening) and examine the added benefit of population-level preventive cancer care that is directed by specific clinician knowledge of individual patient needs. Moreover, rather than compare our system to currently sub-optimal "usual care" practice, our goal is to test whether the impact of our intervention exceeds the current state-of-the-art of IT-based population management. Therefore, control group practices will receive augmented standard care defined as a population-level reminder system with automated patient contacts.

In augmented standard care control practices, the investigators will implement a system that includes: 1) a population-based perspective to identify all eligible patients overdue for screening, 2) an automated, centralized process to contact selected patients by letter, 3) a result management system that automatically tracks test scheduling and completion, 4) a web-based, easily accessible tool allowing practice personnel to contact patients not completing testing, and 5) use of patient navigators for high risk patients not responding to initial outreach. In the control arm, the process of escalating the reminder intervention from a letter, to contact by phone call, to a patient navigator, will occur in a standard algorithmic fashion without provider input. While not yet the standard of care nationwide, prior studies have proven the efficacy of such an approach. In intervention practices, the investigators will enhance augmented standard care by implementing a novel system that will enable physicians and clinical population managers to individualize care for each patient in their panel using tools to classify and organize patients by their clinical attributes. The investigators hypothesize that this personalized identification of patients by both their clinical outcome and clinical process risk status will improve the efficacy and efficiency of resource allocation decisions. The key additions to the health IT system for intervention practices will be: 1) a clinical systems IT platform to organize and present clinical data for each clinician's patient panel, 2) an accessible Web-based tool allowing clinicians (physicians and clinical population managers) to view, organize, and investigate their patient panels, and 3) a simple process where the clinician can make a tailored screening decision and designate the method of clinical intervention based upon the patient's risk profile.

Population-Based Patient-Centric Care: Comprehensive Preventive Cancer Screening Using Health IT

Technology for Optimizing Population Care in a Resource-limited Environment

Average cancer screening test completion rate over the 1-year follow-up period for each eligible patient in all eligible cancers (breast, cervical, colorectal)

Percentage of patients completing all eligible cancer screening modalities at 1-year follow-up will be compared between study arms

1. Eligible population: prevalent (overdue at baseline) or incident (become overdue during study) cancer screening rates will be compared between study arms.
2. Patient-physician linkage status: Analyses will be stratified to compare PCP-linked patients between study arms and practice-linked patients between study arms.
3. Patient risk status: Patients at high risk for not completing screening will be compared between study arms.

1. Percentage of providers (PCPs and clinical population managers) in intervention practices using TOP-CARE system and percentage of eligible patients in clinician panel screened
2. Percentage of providers (practice personnel and patient navigators) in intervention and control practices using TOP-CARE system and percentage of eligible patients assigned to provider who were contacted
3. Percentage of eligible patients in clinician (PCP and clinical population manager) panel deferred and reasons selected

Study title	NCT ID	Conditions	Interventions	Eligibility	Organization
Understanding the Value of Community Vital Signs in Primary Care	NCT02512835	Colon Cancer Breast Cancer Cervical Cancer Prostate Cancer Cardiovascular ...	Community vital...	18 Years - 99 Years	Virginia Commonwealth University
Investigating the Impact of Tailored Reports on Anxiety Amongst Cancer Patients and Their Confidants	NCT00127465	Breast Neoplasm... Prostatic Neopl... Laryngeal Neopl... Cervix Neoplasm...	Issue of inform...	18 Years -	University of Plymouth
My Team of Care: a Pilot Randomized Controlled Trial of an Online Communication Tool for Collaborative Care	NCT02372994	Neoplasms Communication	Loop	18 Years -	Mount Sinai Hospital, Canada
Understanding the Value of Community Vital Signs in Primary Care	NCT02512835	Colon Cancer Breast Cancer Cervical Cancer Prostate Cancer Cardiovascular ...	Community vital...	18 Years - 99 Years	Virginia Commonwealth University
Investigating the Impact of Tailored Reports on Anxiety Amongst Cancer Patients and Their Confidants	NCT00127465	Breast Neoplasm... Prostatic Neopl... Laryngeal Neopl... Cervix Neoplasm...	Issue of inform...	18 Years -	University of Plymouth
Development of an Artificial Intelligence System for Intelligent Pathological Diagnosis and Therapeutic Effect Prediction Based on Multimodal Data Fusion of Common Tumors and Major Infectious Diseases in the Respiratory System Using Deep Learning Technology.	NCT05046366	Artificial Inte... Deep Learning Pathology, Mole... Medical Informa... Database Lung Cancer Pulmonary Tuber... Covid19		18 Years - 90 Years	Wuhan Union Hospital, China
My Team of Care: a Pilot Randomized Controlled Trial of an Online Communication Tool for Collaborative Care	NCT02372994	Neoplasms Communication	Loop	18 Years -	Mount Sinai Hospital, Canada
Population-Based Patient-Centric Care: Comprehensive Preventive Cancer Screening Using Health IT	NCT01372527	Breast Cancer Colorectal Canc... Cervical Cancer	TopCare: Use of...	21 Years - 75 Years	Massachusetts General Hospital

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