The Caris registry: Building a biomarker-focused database to advance patient care.

2012 ◽  
Vol 30 (15_suppl) ◽  
pp. e21152-e21152
Author(s):  
Sheri Sanders ◽  
Wendy Schroeder ◽  
Alan Wright ◽  
Jeff Field
Keyword(s):  
Clinical Outcomes ◽  
Clinical Decision Making ◽  
Information Access ◽  
Data Entry ◽  
Clinical Decision ◽  
Outcome Data ◽  
Medical Community ◽  
Tumor Biomarker ◽  
Biomarker Analysis ◽  
Biomarker Research

e21152 Background: The medical community is continually searching for the best way to treat cancer. The value and utility of biomarkers in guiding treatment decisions is widely accepted but remains a challenge for the bedside clinician and requires ongoing validation and correlation to clinical outcomes. Caris Life Sciences has a dedicated team of scientists who study volumes of scientific literature, synthesize biomarker research and by way of an evidence-based electronic rules engine, translates the application of the literature to biomarker analysis of tumor tissue (The Target Now Report) in support of biomarker-drug association evidence useful in clinical decision-making. Subsequently, Caris initiated the Caris Registry to capture clinical disease, treatment and outcome data from patients who have a Target Now Report. Methods: The Caris Registry is a web-based data entry platform based on an IRB approved protocol. The eligible subject for the Registry will have a qualified Target Now Report. All clinical data elements are defined and supported by the NCI caBIG standardized data dictionary. Disease history/status, treatments and outcomes are captured at enrollment with Day 1 defined as the date of the Target Now Report and every 9 months for 5 years or death whichever is first. Results: As of January 19, 2012, there are 68 participating centers across the country and 43 centers pending IRB submission. There are 852 Target Now cases enrolled with the following cancer lineage distribution: Breast 209, Ovary 169, Lung 117, Colon 79, Endometrium 33, and other 245. There are 323 completed follow up reports and 175 completed end of study reports capturing vital status and cancer related deaths. Conclusions: Caris has successfully launched a scientifically valid and regulatory compliant Registry and database intended to become a robust library of tumor biomarker results linked to clinical outcomes data. As the library grows, data mining could provide vital information access to researchers, pharmaceutical firms, government, academia and insurers for use in drug development, molecular and biomarker research, economic impact assessments, healthcare policy discussion and most importantly directing personalized cancer treatment.

Debates in Dysphagia Management: How Do You Use Evidence-Based Practice in Your Dysphagia Patient Care?

2011 ◽  
Vol 20 (4) ◽  
pp. 121-123
Author(s):  
Jeri A. Logemann
Keyword(s):  
Decision Making ◽  
Patient Care ◽  
Clinical Outcomes ◽  
Clinical Judgment ◽  
Clinical Decision Making ◽  
Evidence Based Practice ◽  
Clinical Decision ◽  
Evidence Based ◽  
Clinical Method ◽  
Do So

Evidence-based practice requires astute clinicians to blend our best clinical judgment with the best available external evidence and the patient's own values and expectations. Sometimes, we value one more than another during clinical decision-making, though it is never wise to do so, and sometimes other factors that we are unaware of produce unanticipated clinical outcomes. Sometimes, we feel very strongly about one clinical method or another, and hopefully that belief is founded in evidence. Some beliefs, however, are not founded in evidence. The sound use of evidence is the best way to navigate the debates within our field of practice.

Decision-making under pressure: medical errors in uncertain and dynamic environments

2018 ◽  
Vol 42 (4) ◽  
pp. 395 ◽  
Author(s):  
Alicia M. Zavala ◽  
Gary E. Day ◽  
David Plummer ◽  
Anita Bamford-Wade
Keyword(s):  
Decision Making ◽  
Human Factors ◽  
Clinical Outcomes ◽  
Medical Errors ◽  
Thematic Analysis ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Dynamic Environments ◽  
Emergency Situations ◽  
Case Complexity

Objective This paper provides a narrative overview of the literature concerning clinical decision-making processes when staff come under pressure, particularly in uncertain, dynamic and emergency situations. Methods Studies between 1980 and 2015 were analysed using a six-phase thematic analysis framework to achieve an in-depth understanding of the complex origins of medical errors that occur when people and systems are under pressure and how work pressure affects clinical performance and patient outcomes. Literature searches were conducted using a Summons Search Service platform; search criteria included a variety of methodologies, resulting in the identification of 95 papers relevant to the present review. Results Six themes emerged in the present narrative review using thematic analysis: organisational systems, workload, time pressure, teamwork, individual human factors and case complexity. This analysis highlights that clinical outcomes in emergency situations are the result of a variety of interconnecting factors. These factors may affect the ability of clinical staff in emergency situations to provide quality, safe care in a timely manner. Conclusions The challenge for researchers is to build the body of knowledge concerning the safe management of patients, particularly where clinicians are working under pressure. This understanding is important for developing pathways that optimise clinical decision making in uncertain and dynamic environments. What is known about the topic? Emergency departments (EDs) are characterised by high complexity, high throughput and greater uncertainty compared with routine hospital wards or out-patient situations, and the ED is therefore prone to unpredictable workflows and non-replicable conditions when presented with unique and complex cases. What does this paper add? Clinical decision making can be affected by pressures with complex origins, including organisational systems, workload, time constraints, teamwork, human factors and case complexity. Interactions between these factors at different levels of the decision-making process can increase the complexity of problems and the resulting decisions to be made. What are the implications for practitioners? The findings of the present study provide further evidence that consideration of medical errors should be seen primarily from a ‘whole-of-system’ perspective rather than as being primarily the responsibility of individuals. Although there are strategies in place in healthcare organisations to eliminate errors, they still occur. In order to achieve a better understanding of medical errors in clinical practice in times of uncertainty, it is necessary to identify how diverse pressures can affect clinical decisions, and how these interact to influence clinical outcomes.

Clinical Decision Making by Emergency Room Physicians and Residents

2008 ◽  
pp. 217-223
Author(s):  
Ken J. Farion ◽  
Michael J. Hine ◽  
Wojtek Michalowski ◽  
Szymon Wilk
Keyword(s):  
Decision Making ◽  
Information Technology ◽  
Decision Support ◽  
Clinical Decision Making ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Medical Community ◽  
Medical Decision ◽  
Best Interest ◽  
Complex Process

Clinical decision-making is a complex process that is reliant on accurate and timely information. Clinicians are dependent (or should be dependent) on massive amounts of information and knowledge to make decisions that are in the best interest of the patient. Increasingly, information technology (IT) solutions are being used as a knowledge transfer mechanism to ensure that clinicians have access to appropriate knowledge sources to support and facilitate medical decision making. One particular class of IT that the medical community is showing increased interest in is clinical decision support systems (CDSSs).

Clinical Assessment of Upper Extremity Injury Outcomes

2011 ◽  
Vol 20 (1) ◽  
pp. 61-73 ◽  
Author(s):  
Charles Thigpen ◽  
Ellen Shanley
Keyword(s):  
Decision Making ◽  
Clinical Outcome ◽  
Outcome Assessment ◽  
Clinical Outcomes ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Assessment Tools ◽  
Bottom Line ◽  
Patient Scenario ◽  
Clinical Outcomes Assessment

Patient Scenario:The patient presented is a high school baseball pitcher who was unable to throw because of shoulder pain. He subsequently failed nonoperative management but was able to return to pitching after surgery and successful rehabilitation.Clinical Outcomes Assessment:The Disabilities of Arm, Shoulder and Hand (DASH) and the Pennsylvania Shoulder Score (PENN) were selected as clinical outcome assessment tools to quantify the patient’s perceived ability to perform common daily tasks and sport tasks and current symptoms such as pain and patient satisfaction.Clinical Decision Making:The DASH and PENN provide important information that can be used to target specific interventions, set appropriate patient goals, assess between-sessions changes in patient status, and quantify patients’ functional loss.Clinical Bottom Line:Best clinical practice involves the use of clinical outcome assessment tools to garner an objective measure of the impact of a patient’s disease process on functional expectations. This process should facilitate a patient-centered approach by clinicians while they select the optimal intervention strategies and establish prognostic timelines.

scholarly journals Centralised RECIST Assessment and Clinical Outcomes with Lenvatinib Monotherapy in Recurrent and Metastatic Adenoid Cystic Carcinoma

Cancers ◽  
2021 ◽  
Vol 13 (17) ◽  
pp. 4336
Author(s):  
Laura Feeney ◽  
Yatin Jain ◽  
Matthew Beasley ◽  
Oliver Donnelly ◽  
Anthony Kong ◽  
...  
Keyword(s):  
Decision Making ◽  
Adenoid Cystic Carcinoma ◽  
Clinical Outcomes ◽  
Clinical Decision Making ◽  
Kinase Inhibitor ◽  
Objective Response ◽  
Response Rates ◽  
Clinical Decision ◽  
Adenoid Cystic ◽  
Phase Ii Studies

Adenoid cystic carcinoma (ACC) is a rare cancer of secretory glands. Recurrent or metastatic (R/M) ACC is generally considered resistant to cytotoxic chemotherapy. Recent phase II studies have reported improved objective response rates (ORR) with the use of the multi-kinase inhibitor lenvatinib. We sought to evaluate real-world experience of R/M ACC patients treated with lenvatinib monotherapy within the UK National Health Service (NHS) to determine the response rates by Response Evaluation Criteria of Solid Tumour (RECIST) and clinical outcomes. Twenty-three R/M ACC patients from eleven cancer centres were included. All treatment assessments for clinical decision making related to drug therapy were undertaken at the local oncology centre. Central radiology review was performed by an independent clinical trial radiologist and blinded to the clinical decision making. In contrast to previously reported ORR of 12–15%, complete or partial response was not observed in any patients. Eleven patients (52.4%) had stable disease and 5 patients (23.8%) had progression of disease as the best overall response. The median time on treatment was 4 months and the median survival from discontinuation was 1 month. The median PFS and OS from treatment initiation were 4.5 months and 12 months respectively. Multicentre collaborative studies such as this are required to evaluate rare cancers with no recommended standard of care therapy and variable disease courses.

scholarly journals Cognitive Computing-Based CDSS in Medical Practice

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Jun Chen ◽  
Chao Lu ◽  
Haifeng Huang ◽  
Dongwei Zhu ◽  
Qing Yang ◽  
...  
Keyword(s):  
Health Care ◽  
Medical Practice ◽  
General Framework ◽  
Clinical Decision Making ◽  
High Efficiency ◽  
Clinical Decision ◽  
Machine Intelligence ◽  
Medical Community ◽  
Current Status ◽  
Cognitive Computing

Importance. The last decade has witnessed the advances of cognitive computing technologies that learn at scale and reason with purpose in medicine studies. From the diagnosis of diseases till the generation of treatment plans, cognitive computing encompasses both data-driven and knowledge-driven machine intelligence to assist health care roles in clinical decision-making. This review provides a comprehensive perspective from both research and industrial efforts on cognitive computing-based CDSS over the last decade. Highlights. (1) A holistic review of both research papers and industrial practice about cognitive computing-based CDSS is conducted to identify the necessity and the characteristics as well as the general framework of constructing the system. (2) Several of the typical applications of cognitive computing-based CDSS as well as the existing systems in real medical practice are introduced in detail under the general framework. (3) The limitations of the current cognitive computing-based CDSS is discussed that sheds light on the future work in this direction. Conclusion. Different from medical content providers, cognitive computing-based CDSS provides probabilistic clinical decision support by automatically learning and inferencing from medical big data. The characteristics of managing multimodal data and computerizing medical knowledge distinguish cognitive computing-based CDSS from other categories. Given the current status of primary health care like high diagnostic error rate and shortage of medical resources, it is time to introduce cognitive computing-based CDSS to the medical community which is supposed to be more open-minded and embrace the convenience and low cost but high efficiency brought by cognitive computing-based CDSS.

scholarly journals The importance of effective registries in pulmonary diseases and how to optimize their output

2019 ◽  
Vol 16 ◽  
pp. 147997311988177 ◽  
Author(s):  
Joanna Chorostowska-Wynimko ◽  
Marion Wencker ◽  
Ildikó Horváth
Keyword(s):  
Health Care Policy ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Outcome Data ◽  
The European Union ◽  
Patient Registries ◽  
General Data Protection Regulation ◽  
Disease Epidemiology ◽  
Alpha 1 Antitrypsin Deficiency ◽  
The Impact

Randomized controlled trials (RCTs) are essential for the approval of new therapies; however, because of their design, they provide little insight concerning disease epidemiology/etiology and current clinical practice. Particularly, in lung disease, rigid inclusion/exclusion criteria can limit the generalizability of pivotal trial data. Noninterventional studies (NIS), conducted through the well-established mechanism of patient registries, are undervalued as a means to close data gaps left by RCTs by providing essential data that can guide patient care at different levels from clinical decision-making to health-care policy. While NIS contribute valuable data in all disease areas, their importance in rare diseases cannot be underestimated. In respiratory disease, registries have been essential in understanding the natural history and different phenotypes of rare conditions, such as alpha 1 antitrypsin deficiency, cystic fibrosis, and idiopathic pulmonary fibrosis. Importantly, additional therapeutic outcome data were generated. While measures for enhancing data quality in RCTs have evolved significantly, the approach and effectiveness of registries is variable. Within this article, we review the contribution of registries to pulmonary disease and make recommendations for their effective management. Additionally, we assess limitations of registry data as well as challenges to registry operation, including the impact of the European Union General Data Protection Regulation.

scholarly journals Predicting Clinical Outcomes Using Molecular Biomarkers

2016 ◽  
Vol 8 ◽  
pp. BIC.S33380 ◽  
Author(s):  
Harry B. Burke
Keyword(s):  
Decision Making ◽  
Clinical Outcomes ◽  
Clinical Decision Making ◽  
Clinical Care ◽  
Clinical Decision ◽  
Molecular Biomarkers ◽  
The Past ◽  
Diagnosis And Prognosis ◽  
Molecular Therapies ◽  
Physician Patient

Over the past 20 years, there has been an exponential increase in the number of biomarkers. At the last count, there were 768,259 papers indexed in PubMed.gov directly related to biomarkers. Although many of these papers claim to report clinically useful molecular biomarkers, embarrassingly few are currently in clinical use. It is suggested that a failure to properly understand, clinically assess, and utilize molecular biomarkers has prevented their widespread adoption in treatment, in comparative benefit analyses, and their integration into individualized patient outcome predictions for clinical decision-making and therapy. A straightforward, general approach to understanding how to predict clinical outcomes using risk, diagnostic, and prognostic molecular biomarkers is presented. In the future, molecular biomarkers will drive advances in risk, diagnosis, and prognosis, they will be the targets of powerful molecular therapies, and they will individualize and optimize therapy. Furthermore, clinical predictions based on molecular biomarkers will be displayed on the clinician's screen during the physician–patient interaction, they will be an integral part of physician–patient-shared decision-making, and they will improve clinical care and patient outcomes.

Clinical Assessment of Low-Back-Pain Treatment Outcomes in Athletes

2011 ◽  
Vol 20 (1) ◽  
pp. 74-88 ◽  
Author(s):  
Luzita I. Vela ◽  
Douglas E. Haladay ◽  
Craig Denegar
Keyword(s):  
Low Back Pain ◽  
Back Pain ◽  
Clinical Outcomes ◽  
Clinical Decision Making ◽  
Outcomes Assessment ◽  
Clinical Decision ◽  
Low Back ◽  
Bottom Line ◽  
Patient Scenario ◽  
Clinical Outcomes Assessment

Patient Scenario:A 21-year-old male rodeo athlete complains of acute low back pain (LBP) after a bareback event. The athlete wishes to compete in a rodeo event in 4 d.Clinical Outcomes Assessment:Given the questionable validity and reliability of traditional clinical examination techniques for LBP, a treatment subgroup classification system combined with clinical outcomes assessment provides greater insight into suitable clinical interventions and patient response to treatment. Four LBP treatment subgroups based on the patient’s clinical presentation and symptoms have been established: manipulation, stabilization, specific exercise, and traction. Manipulation subgroup research has produced a valid clinical prediction rule (CPR). The Visual Analog Scale, Numeric Rating Scale (NRS), Oswestry Low Back Pain Disability Index (ODI), Roland Morris Disability Questionnaire, Short Form 36 (SF-36), and Global Rating of Change Scale are valid, reliable, and responsive outcomes instruments with established values for minimum clinically important difference (MCID). These instruments document important changes in disablement and health-related quality of life in patients with low back injury, as well as demonstrate treatment outcomes.Clinical Decision Making:On examination the athlete presents with moderate pain and disability as measured by the NRS, ODI, and SF-36 and meets all 5 criteria for the manipulation subgroup, indicating a high likelihood of success with manipulative therapy when following the guidelines presented in the CPR. Expected outcomes values, based on MCID values, were met after 1 treatment. Preferred outcomes, based on physical activity requirements for sport, were met on day 4.Clinical Bottom Line:LBP generators are difficult to establish using traditional clinical examination techniques. The combined use of clinical criteria, using an LBP subgroup system, and baseline outcomes measures should guide treatment. Benchmarks should be guided by established MCID values for each instrument.

scholarly journals The short- to midterm effectiveness of stemless prostheses compared to stemmed prostheses for patients who underwent total shoulder arthroplasty: a meta-analysis

2019 ◽  
Vol 14 (1) ◽  
Author(s):  
Wei Peng ◽  
Yufu Ou ◽  
Chenglong Wang ◽  
Jianxun Wei ◽  
Xiaoping Mu ◽  
...  
Keyword(s):  
Clinical Outcomes ◽  
Shoulder Arthroplasty ◽  
Clinical Decision Making ◽  
Constant Score ◽  
Data Extraction ◽  
Meta Analysis ◽  
Total Shoulder Arthroplasty ◽  
Clinical Decision ◽  
Clinical Results ◽  
Cochrane Library

Abstract Background To systematically compare the short- to midterm effectiveness of stemless prostheses to that of stemmed prostheses for patients who underwent total shoulder arthroplasty (TSA) and to provide a guideline for clinical decision-making. Methods PubMed, the Cochrane Library, and Web of Science were searched with the given search terms until July 2019 to identify published articles evaluating the clinical outcomes for stemless prostheses compared with stemmed prostheses for patients who underwent TSA. Data extraction and the quality assessment of the included studies were independently performed by two authors. Stata software 14.0 was used to analyze and synthesize the data. Results Two randomized controlled trials and six case-controlled studies with a total of 347 shoulders were included in this meta-analysis. The results of this meta-analysis showed that there were no significant differences between the stemless and stemmed prostheses in terms of the Constant score, pain score, strength, activities of daily living, postoperative range of motion (ROM), and postoperative maximum active ROM. Conclusions This is the first meta-analysis reporting the clinical results of stemless TSA in the short- to midterm follow-up period. Both types of shoulder prostheses were similar in achieving satisfactory clinical outcomes.

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