Computer-Assisted Reporting and Decision Support in Standardized Radiology Reporting for Cancer Imaging

2021 ◽  
pp. 426-434
Author(s):  
Bernardo C. Bizzo ◽  
Renata R. Almeida ◽  
Tarik K. Alkasab
Keyword(s):  
Decision Support ◽  
Improve Patient Safety ◽  
Cancer Imaging ◽  
Clinical Decision ◽  
Structured Reporting ◽  
Computer Assisted ◽  
Radiology Reporting ◽  
Multiple Sources ◽  
Structured Reports ◽  
Value Based Care

PURPOSE Recent advances in structured reporting are providing an opportunity to enhance cancer imaging assessment to drive value-based care and improve patient safety. METHODS The computer-assisted reporting and decision support (CAR/DS) framework has been developed to enable systematic ingestion of guidelines as clinical decision structured reporting tools embedded within the radiologist's workflow. RESULTS CAR/DS tools can reduce the radiology reporting variability and increase compliance with clinical guidelines. The lung cancer use-case is used to describe various scenarios of a cancer imaging structured reporting pathway, including incidental findings, screening, staging, and restaging or continued care. Various aspects of these tools are also described using cancer-related examples for different imaging modalities and applications such as calculators. Such systems can leverage artificial intelligence (AI) algorithms to assist with the generation of structured reports and there are opportunities for new AI applications to be created using the structured data associated with CAR/DS tools. CONCLUSION These AI-enabled systems are starting to allow information from multiple sources to be integrated and inserted into structured reports to drive improvements in clinical decision support and patient care.

scholarly journals Structured Reporting of Rectal Cancer Staging and Restaging: A Consensus Proposal

Cancers ◽  
2021 ◽  
Vol 13 (9) ◽  
pp. 2135
Author(s):  
Vincenza Granata ◽  
Damiano Caruso ◽  
Roberto Grassi ◽  
Salvatore Cappabianca ◽  
Alfonso Reginelli ◽  
...  
Keyword(s):  
Rectal Cancer ◽  
Correlation Coefficient ◽  
Clinical Decision Making ◽  
Intraclass Correlation ◽  
Clinical Decision ◽  
Cancer Staging ◽  
Structured Reporting ◽  
Quality Analysis ◽  
Promising Tool ◽  
Structured Reports

Background: Structured reporting (SR) in oncologic imaging is becoming necessary and has recently been recognized by major scientific societies. The aim of this study was to build MRI-based structured reports for rectal cancer (RC) staging and restaging in order to provide clinicians all critical tumor information. Materials and Methods: A panel of radiologist experts in abdominal imaging, called the members of the Italian Society of Medical and Interventional Radiology, was established. The modified Delphi process was used to build the SR and to assess the level of agreement in all sections. The Cronbach’s alpha (Cα) correlation coefficient was used to assess the internal consistency of each section and to measure the quality analysis according to the average inter-item correlation. The intraclass correlation coefficient (ICC) was also evaluated. Results: After the second Delphi round of the SR RC staging, the panelists’ single scores and sum of scores were 3.8 (range 2–4) and 169, and the SR RC restaging panelists’ single scores and sum of scores were 3.7 (range 2–4) and 148, respectively. The Cα correlation coefficient was 0.79 for SR staging and 0.81 for SR restaging. The ICCs for the SR RC staging and restaging were 0.78 (p < 0.01) and 0.82 (p < 0.01), respectively. The final SR version was built and included 53 items for RC staging and 50 items for RC restaging. Conclusions: The final version of the structured reports of MRI-based RC staging and restaging should be a helpful and promising tool for clinicians in managing cancer patients properly. Structured reports collect all Patient Clinical Data, Clinical Evaluations and relevant key findings of Rectal Cancer, both in staging and restaging, and can facilitate clinical decision-making.

scholarly journals Advances in Sharing Multi-sourced Health Data on Decision Support Science 2016-2017

2018 ◽  
Vol 27 (01) ◽  
pp. 016-024 ◽  
Author(s):  
Prabhu Shankar ◽  
Nick Anderson
Keyword(s):  
Decision Support ◽  
Clinical Decision Support ◽  
Policy Development ◽  
Public Awareness ◽  
Clinical Decision ◽  
Data Sources ◽  
Computational Techniques ◽  
Scientific Conference ◽  
Multiple Sources ◽  
Commercial Applications

Introduction: Clinical decision support science is expanding to include integration from broader and more varied data sources, diverse platforms and delivery modalities, and is responding to emerging regulatory guidelines and increased interest from industry. Objective: Evaluate key advances and challenges of accessing, sharing, and managing data from multiple sources for development and implementation of Clinical Decision Support (CDS) systems in 2016-2017. Methods: Assessment of literature and scientific conference proceedings, current and pending policy development, and review of commercial applications nationally and internationally. Results: CDS research is approaching multiple landmark points driven by commercialization interests, emerging regulatory policy, and increased public awareness. However, the availability of patient-related “Big Data” sources from genomics and mobile health, expanded privacy considerations, applications of service-based computational techniques and tools, the emergence of “app” ecosystems, and evolving patient-centric approaches reflect the distributed, complex, and uneven maturity of the CDS landscape. Nonetheless, the field of CDS is yet to mature. The lack of standards and CDS-specific policies from regulatory bodies that address the privacy and safety concerns of data and knowledge sharing to support CDS development may continue to slow down the broad CDS adoption within and across institutions. Conclusion: Partnerships with Electronic Health Record and commercial CDS vendors, policy makers, standards development agencies, clinicians, and patients are needed to see CDS deployed in the evolving learning health system.

scholarly journals Real-time pharmacy surveillance and clinical decision support to reduce adverse drug events in acute kidney injury

2012 ◽  
Vol 03 (02) ◽  
pp. 221-238 ◽  
Author(s):  
Z.L. Cox ◽  
E.B. Neal ◽  
L.R. Waitman ◽  
N.B. Peterson ◽  
G. Bhave ◽  
...  
Keyword(s):  
Acute Kidney Injury ◽  
Decision Support ◽  
Clinical Decision Support ◽  
Adverse Drug Events ◽  
Improve Patient Safety ◽  
Tertiary Care ◽  
Intervention Group ◽  
Kidney Injury ◽  
Clinical Decision ◽  
Care Hospital

Summary Objectives: Clinical decision support (CDS), such as computerized alerts, improves prescribing in the setting of acute kidney injury (AKI), but considerable opportunity remains to improve patient safety. The authors sought to determine whether pharmacy surveillance of AKI patients could detect and prevent medication errors that are not corrected by automated interventions. Methods: The authors conducted a randomized clinical trial among 396 patients admitted to an academic, tertiary care hospital between June 1, 2010 and August 31, 2010 with an acute 0.5 mg/dl change in serum creatinine over 48 hours and a nephrotoxic or renally cleared medication order. Patients randomly assigned to the intervention group received surveillance from a clinical pharmacist using a web-based surveillance tool to monitor drug prescribing and kidney function trends. CDS alerting and standard pharmacy services were active in both study arms. Outcome measures included blinded adjudication of potential adverse drug events (pADEs), adverse drug events (ADEs) and time to provider modification or discontinuation of targeted nephrotoxic or renally cleared medications. Results: Potential ADEs or ADEs occurred for 104 (8.0%) of control and 99 (7.1%) of intervention patient-medication pairs (p=0.4). Additionally, the time to provider modification or discontinuation of targeted nephrotoxic or renally cleared medications did not differ between control and intervention patients (33.4 hrs vs. 30.3hrs, p=0.3). Conclusions: Pharmacy surveillance had no incremental benefit over previously implemented CDS alerts

scholarly journals Using REDCap and Apple ResearchKit to integrate patient questionnaires and clinical decision support into the electronic health record to improve sexually transmitted infection testing in the emergency department

2019 ◽  
Vol 27 (2) ◽  
pp. 265-273 ◽  
Author(s):  
Fahd A Ahmad ◽  
Philip R O Payne ◽  
Ian Lackey ◽  
Rachel Komeshak ◽  
Kenneth Kenney ◽  
...  
Keyword(s):  
At Risk ◽  
Emergency Department ◽  
Decision Support ◽  
Electronic Health Record ◽  
Clinical Decision Support ◽  
Clinical Decision ◽  
Computer Assisted ◽  
Health Record ◽  
Sexually Transmitted ◽  
Electronic Health

Abstract Objective Audio-enhanced computer-assisted self-interviews (ACASIs) are useful adjuncts for clinical care but are rarely integrated into the electronic health record (EHR). We created a flexible framework for integrating an ACASIs with clinical decision support (CDS) into the EHR. We used this program to identify adolescents at risk for sexually transmitted infections (STIs) in the emergency department (ED). We provide an overview of the software platform and qualitative user acceptance. Materials and Methods We created an ACASI with a CDS algorithm to identify adolescents in need of STI testing. We offered it to 15- to 21-year-old patients in our ED, regardless of ED complaint. We collected user feedback via the ACASI. These were programmed into REDCap (Research Electronic Data Capture), and an iOS application utilizing Apple ResearchKit generated a tablet compatible representation of the ACASI for patients. A custom software program created an HL7 (Health Level Seven) message containing a summary of responses, CDS recommendations, and STI test orders, which were transmitted to the EHR. Results In the first year, 1788 of 6227 (28.7%) eligible adolescents completed the survey. Technical issues led to decreased use for several months. Patients rated the system favorably, with 1583 of 1787 (88.9%) indicating that they were “somewhat” or “very comfortable” answering questions electronically and 1291 of 1787 (72.2%) preferring this format over face-to-face interviews or paper questionnaires. Conclusions We present a novel use for REDCap to combine patient-answered questionnaires and CDS to improve care for adolescents at risk for STIs. Our program was well received and the platform can be used across disparate patients, topics, and information technology infrastructures.

Experience from Computer-Based Patient Records for Computer-Assisted Decision Making

1993 ◽  
Vol 32 (01) ◽  
pp. 14-15 ◽  
Author(s):  
J. van der Lei
Keyword(s):  
Decision Making ◽  
Decision Support ◽  
Decision Support Systems ◽  
Clinical Decision Support ◽  
Support Systems ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Computer Assisted ◽  
Patient Records ◽  
Computer Based

Abstract:Response to Heathfield HA, Wyatt J. Philosophies for the design and development of clinical decision-support systems. Meth Inform Med 1993; 32: 1-8.

scholarly journals Comparing the Effectiveness of Computerized Adverse Drug Event Monitoring Systems to Enhance Clinical Decision Support for Hospitalized Patients

2010 ◽  
Vol 01 (03) ◽  
pp. 293-303 ◽  
Author(s):  
G. N. Petratos ◽  
Y. Kim ◽  
R. S. Evans ◽  
S. D. Williams ◽  
R. M. Gardner
Keyword(s):  
Decision Support ◽  
Clinical Decision Support ◽  
Language Processing ◽  
Adverse Drug Event ◽  
Tertiary Care ◽  
Care Facility ◽  
Clinical Decision ◽  
Drug Event ◽  
Computer Assisted ◽  
Tertiary Care Facility

Summary Objective: Performance of computerized adverse drug event (ADE) monitoring of electronic health records through a prospective ADE Monitor and ICD9-coded clinical text review operating independently and simultaneously on the same patient population for a 10-year period are compared. Requirements are compiled for clinical decision support in pharmacy systems to enhance ADE detection. Methods: A large tertiary care facility in Utah, with a history of quality improvement using its advanced hospital information system, was leveraged in this study. ICD9-based review of clinical charts (ICD9 System) was compared quantitatively and qualitatively to computer-assisted pharmacist-verified ADEs (ADE Monitor). The capture-recapture statistical method was applied to the data to determine an estimated prevalence of ADEs. Results: A total estimated ADE prevalence of 5.53% (13,420/242,599) was calculated, with the ICD9 system identifying 2,604 or 19.4%, and the ADE monitor 3,386 or 25.2% of all estimated ADEs. Both methods commonly identified 4.9% of all estimated ADEs and matched 62.0% of the time, each having its strength in detecting a slightly different domain of ADEs. 70% of the ADE documentation in the clinical notes was found in the discharge summaries. Conclusion: Coupled with spontaneous reporting, computerized methods account for approximately half of all ADEs that can currently be detected. To enhance ADE monitoring and patient safety in a hospitalized setting, pharmacy information systems should incorporate prospective structuring and coding of the text in clinical charts and using that data alongside computer-generated alerts of laboratory results and drug orders. Natural language processing can aid computerized detection by automating the coding, in real-time, of physician text from clinical charts so that decision support rules can be created and applied. New detection strategies and enhancements to existing systems should be researched to enhance the detection of ADEs since approximately half are not currently detected.

scholarly journals Cloud-Based System for Effective Surveillance and Control of COVID-19: Useful Experiences From Hubei, China

10.2196/18948 ◽  
2020 ◽  
Vol 22 (4) ◽  
pp. e18948 ◽  
Author(s):  
Mengchun Gong ◽  
Li Liu ◽  
Xin Sun ◽  
Yue Yang ◽  
Shuang Wang ◽  
...  
Keyword(s):  
Decision Support ◽  
Mobile Devices ◽  
Clinical Decision Support ◽  
Syndromic Surveillance ◽  
Improve Patient Care ◽  
Clinical Effectiveness ◽  
Clinical Decision ◽  
Effective Control ◽  
Multiple Sources ◽  
Related Data

Background Coronavirus disease (COVID-19) has been an unprecedented challenge to the global health care system. Tools that can improve the focus of surveillance efforts and clinical decision support are of paramount importance. Objective The aim of this study was to illustrate how new medical informatics technologies may enable effective control of the pandemic through the development and successful 72-hour deployment of the Honghu Hybrid System (HHS) for COVID-19 in the city of Honghu in Hubei, China. Methods The HHS was designed for the collection, integration, standardization, and analysis of COVID-19-related data from multiple sources, which includes a case reporting system, diagnostic labs, electronic medical records, and social media on mobile devices. Results HHS supports four main features: syndromic surveillance on mobile devices, policy-making decision support, clinical decision support and prioritization of resources, and follow-up of discharged patients. The syndromic surveillance component in HHS covered over 95% of the population of over 900,000 people and provided near real time evidence for the control of epidemic emergencies. The clinical decision support component in HHS was also provided to improve patient care and prioritize the limited medical resources. However, the statistical methods still require further evaluations to confirm clinical effectiveness and appropriateness of disposition assigned in this study, which warrants further investigation. Conclusions The facilitating factors and challenges are discussed to provide useful insights to other cities to build suitable solutions based on cloud technologies. The HHS for COVID-19 was shown to be feasible and effective in this real-world field study, and has the potential to be migrated.

scholarly journals Cloud-Based System for Effective Surveillance and Control of COVID-19: Useful Experiences From Hubei, China (Preprint)

2020 ◽  
Author(s):  
Mengchun Gong ◽  
Li Liu ◽  
Xin Sun ◽  
Yue Yang ◽  
Shuang Wang ◽  
...  
Keyword(s):  
Decision Support ◽  
Mobile Devices ◽  
Clinical Decision Support ◽  
Syndromic Surveillance ◽  
Improve Patient Care ◽  
Clinical Effectiveness ◽  
Clinical Decision ◽  
Effective Control ◽  
Multiple Sources ◽  
Related Data

BACKGROUND Coronavirus disease (COVID-19) has been an unprecedented challenge to the global health care system. Tools that can improve the focus of surveillance efforts and clinical decision support are of paramount importance. OBJECTIVE The aim of this study was to illustrate how new medical informatics technologies may enable effective control of the pandemic through the development and successful 72-hour deployment of the Honghu Hybrid System (HHS) for COVID-19 in the city of Honghu in Hubei, China. METHODS The HHS was designed for the collection, integration, standardization, and analysis of COVID-19-related data from multiple sources, which includes a case reporting system, diagnostic labs, electronic medical records, and social media on mobile devices. RESULTS HHS supports four main features: syndromic surveillance on mobile devices, policy-making decision support, clinical decision support and prioritization of resources, and follow-up of discharged patients. The syndromic surveillance component in HHS covered over 95% of the population of over 900,000 people and provided near real time evidence for the control of epidemic emergencies. The clinical decision support component in HHS was also provided to improve patient care and prioritize the limited medical resources. However, the statistical methods still require further evaluations to confirm clinical effectiveness and appropriateness of disposition assigned in this study, which warrants further investigation. CONCLUSIONS The facilitating factors and challenges are discussed to provide useful insights to other cities to build suitable solutions based on cloud technologies. The HHS for COVID-19 was shown to be feasible and effective in this real-world field study, and has the potential to be migrated.

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