Clinical Decision Support Technologies Research Articles

Adherence to integrated management of childhood illness (IMCI) guidelines by community health workers in Kano State, Nigeria through use of a clinical decision support (CDS) platform

BackgroundThe World Health Organization (WHO) Integrated Management of Childhood Illness (IMCI) guidelines established in 1992 to decrease preventable under-five child morbidity and mortality, was adopted by Nigeria in 1997. Over 20 years later, while under-five child mortality remains high, less than 25% of first level facilities have trained 60% of community health workers (CHW) who care for sick children with IMCI. This study investigated the impact in CHWs overall adherence to IMCI guidelines, particularly for critical danger signs, as well as usability and feasible following the implementation of THINKMD’s IMCI-based digital clinical decision support (CDS) platform.MethodsAdherence to IMCI guidelines was assessed by observational and digital data acquisition of key IMCI clinical data points by 28 CHWs, prior, during, and post CDS platform implementation. Change in IMCI adherence was determined for individual CHW and for the cohort by analyzing the number of IMCI data points acquired by each CHW per clinical evaluation. Consistency of adherence was also calculated by averaging the percentage of total evaluations each data point was observed. Usability and acceptability surveys were administered following use of the CDS platform.ResultsTHINKMD CDS platform implementation notably enhanced the CHWs’ ability to capture key IMCI clinical data elements. We observed a significant increase in the mean percentage of data points captured between the baseline period and during the CDS technology implementation (T-test, t = -31.399, p < 0.016, Holm-Bonferroni correction, two-sided), with the mean values going from 30.7% to 72.4%. Notably, even after the completion of the technology implementation phase, the mean percentage of IMCI elements captured by CHWs remained significantly elevated compared to the baseline, with a 26.72 percentage point increase (from 30.7% to 57.4%, T-test, t = -15.779, p < 0.05, Holm-Bonferroni correction, two-sided). Usability and feasibility of the platform was high. CHWs reported that the CDS platform was easy to learn and use (93%) and enabled them to identify sick children (100%).ConclusionThese results demonstrate that utilization of a digital clinical decision support tool such as THINKMD’s IMCI based CDS platform can significantly increase CHW adherence to IMCI guidelines over paper-based utilization, increase clinical quality and capacity, and improve identification of key danger signs for under-five children while being highly accepted and adopted.

Patient-centered clinical decision support challenges and opportunities identified from workflow execution models.

To use workflow execution models to highlight new considerations for patient-centered clinical decision support policies (PC CDS), processes, procedures, technology, and expertise required to support new workflows. To generate and refine models, we used (1) targeted literature reviews; (2) key informant interviews with 6 external PC CDS experts; (3) model refinement based on authors' experience; and (4) validation of the models by a 26-member steering committee. We identified 7 major issues that provide significant challenges and opportunities for healthcare systems, researchers, administrators, and health IT and app developers. Overcoming these challenges presents opportunities for new or modified policies, processes, procedures, technology, and expertise to: (1) Ensure patient-generated health data (PGHD), including patient-reported outcomes (PROs), are documented, reviewed, and managed by appropriately trained clinicians, between visits and after regular working hours. (2) Educate patients to use connected medical devices and handle technical issues. (3) Facilitate collection and incorporation of PGHD, PROs, patient preferences, and social determinants of health into existing electronic health records. (4) Troubleshoot erroneous data received from devices. (5) Develop dashboards to display longitudinal patient-reported data. (6) Provide reimbursement to support new models of care. (7) Support patient engagement with remote devices. Several new policies, processes, technologies, and expertise are required to ensure safe and effective implementation and use of PC CDS. As we gain more experience implementing and working with PC CDS, we should be able to begin realizing the long-term positive impact on patient health that the patient-centered movement in healthcare promises.

Effect of an Innovative Immunization Practice Model to Improve Population Health: Results of the Project IMPACT Immunizations Scaled Demonstration.

Background: U.S. adult vaccination rates remain low. Community pharmacists have skills and opportunity to improve this shortcoming. This study sought to evaluate an innovative practice model on identification of unmet vaccination needs and their resolution. Methods: This prospective, multi-site, multi-state, observational study was conducted in 22 community pharmacy practices in Iowa and Washington. Adults receiving influenza vaccination, medication therapy review, prescriptions for diabetes or cardiovascular disease, or another clinical encounter with a participating pharmacist from December 2017 through November 2019 were included. Pharmacists reviewed vaccination forecasts generated by clinical decision support technology based on their state immunization information system (IIS) to identify unmet vaccination needs, educate patients, and improve vaccination rates. The primary outcomes were numbers of vaccination forecast reviews, patients educated, unmet vaccination needs identified and resolved, and vaccinations administered. Secondary outcomes included numbers of vaccination declinations; times a forecasted vaccine was not recommended because a contraindication was identified by the pharmacist; and times the patients declined a forecasted vaccine due to self-reported vaccination despite lack of documentation in the state IIS. Descriptive statistics were calculated. Results: Pharmacists reviewed vaccination forecasts for 6,234 patients. The vaccination forecasts predicted there were 11,789 vaccinations needed (1.9 per person). 6,405 of the 11,789 unmet vaccination needs (54.3%) were fulfilled during the study period, including 60% on the same day. Of the forecasted needs, 1,085 (9.2%) were found to be previously administered and 59 (0.5%) contraindicated. The remaining patients received information about their personal vaccination needs and recommendations to be vaccinated. Conclusion: Availability of vaccination histories during patient encounters allowed pharmacists to identify and resolve adult vaccination needs in independent and chain community practice settings.

An Accessible Clinical Decision Support System to Curtail Anesthetic Greenhouse Gases in a Large Health Network: Implementation Study.

Inhaled anesthetics in the operating room are potent greenhouse gases and are a key contributor to carbon emissions from health care facilities. Real-time clinical decision support (CDS) systems lower anesthetic gas waste by prompting anesthesia professionals to reduce fresh gas flow (FGF) when a set threshold is exceeded. However, previous CDS systems have relied on proprietary or highly customized anesthesia information management systems, significantly reducing other institutions' accessibility to the technology and thus limiting overall environmental benefit. In 2018, a CDS system that lowers anesthetic gas waste using methods that can be easily adopted by other institutions was developed at the University of California San Francisco (UCSF). This study aims to facilitate wider uptake of our CDS system and further reduce gas waste by describing the implementation of the FGF CDS toolkit at UCSF and the subsequent implementation at other medical campuses within the University of California Health network. We developed a noninterruptive active CDS system to alert anesthesia professionals when FGF rates exceeded 0.7 L per minute for common volatile anesthetics. The implementation process at UCSF was documented and assembled into an informational toolkit to aid in the integration of the CDS system at other health care institutions. Before implementation, presentation-based education initiatives were used to disseminate information regarding the safety of low FGF use and its relationship to environmental sustainability. Our FGF CDS toolkit consisted of 4 main components for implementation: sustainability-focused education of anesthesia professionals, hardware integration of the CDS technology, software build of the CDS system, and data reporting of measured outcomes. The FGF CDS system was successfully deployed at 5 University of California Health network campuses. Four of the institutions are independent from the institution that created the CDS system. The CDS system was deployed at each facility using the FGF CDS toolkit, which describes the main components of the technology and implementation. Each campus made modifications to the CDS tool to best suit their institution, emphasizing the versatility and adoptability of the technology and implementation framework. It has previously been shown that the FGF CDS system reduces anesthetic gas waste, leading to environmental and fiscal benefits. Here, we demonstrate that the CDS system can be transferred to other medical facilities using our toolkit for implementation, making the technology and associated benefits globally accessible to advance mitigation of health care-related emissions.

Pragmatic AI-augmentation in mental healthcare: Key technologies, potential benefits, and real-world challenges and solutions for frontline clinicians.

The integration of artificial intelligence (AI) technologies into mental health holds the promise of increasing patient access, engagement, and quality of care, and of improving clinician quality of work life. However, to date, studies of AI technologies in mental health have focused primarily on challenges that policymakers, clinical leaders, and data and computer scientists face, rather than on challenges that frontline mental health clinicians are likely to face as they attempt to integrate AI-based technologies into their everyday clinical practice. In this Perspective, we describe a framework for “pragmatic AI-augmentation” that addresses these issues by describing three categories of emerging AI-based mental health technologies which frontline clinicians can leverage in their clinical practice—automation, engagement, and clinical decision support technologies. We elaborate the potential benefits offered by these technologies, the likely day-to-day challenges they may raise for mental health clinicians, and some solutions that clinical leaders and technology developers can use to address these challenges, based on emerging experience with the integration of AI technologies into clinician daily practice in other healthcare disciplines.

Sociotechnical Intervention for Improved Delivery of Preventive Cardiovascular Care to Rural Communities: Participatory Design Approach.

BackgroundClinical practice guidelines recommend antiplatelet and statin therapies as well as blood pressure control and tobacco cessation for secondary prevention in patients with established atherosclerotic cardiovascular diseases (ASCVDs). However, these strategies for risk modification are underused, especially in rural communities. Moreover, resources to support the delivery of preventive care to rural patients are fewer than those for their urban counterparts. Transformative interventions for the delivery of tailored preventive cardiovascular care to rural patients are needed.ObjectiveA multidisciplinary team developed a rural-specific, team-based model of care intervention assisted by clinical decision support (CDS) technology using participatory design in a sociotechnical conceptual framework. The model of care intervention included redesigned workflows and a novel CDS technology for the coordination and delivery of guideline recommendations by primary care teams in a rural clinic.MethodsThe design of the model of care intervention comprised 3 phases: problem identification, experimentation, and testing. Input from team members (n=35) required 150 hours, including observations of clinical encounters, provider workshops, and interviews with patients and health care professionals. The intervention was prototyped, iteratively refined, and tested with user feedback. In a 3-month pilot trial, 369 patients with ASCVDs were randomized into the control or intervention arm.ResultsNew workflows and a novel CDS tool were created to identify patients with ASCVDs who had gaps in preventive care and assign the right care team member for delivery of tailored recommendations. During the pilot, the intervention prototype was iteratively refined and tested. The pilot demonstrated feasibility for successful implementation of the sociotechnical intervention as the proportion of patients who had encounters with advanced practice providers (nurse practitioners and physician assistants), pharmacists, or tobacco cessation coaches for the delivery of guideline recommendations in the intervention arm was greater than that in the control arm.ConclusionsParticipatory design and a sociotechnical conceptual framework enabled the development of a rural-specific, team-based model of care intervention assisted by CDS technology for the transformation of preventive health care delivery for ASCVDs.

Digital Health Interventions to Enhance Prevention in Primary Care: Scoping Review.

BackgroundDisease prevention is a central aspect of primary care practice and is comprised of primary (eg, vaccinations), secondary (eg, screenings), tertiary (eg, chronic condition monitoring), and quaternary (eg, prevention of overmedicalization) levels. Despite rapid digital transformation of primary care practices, digital health interventions (DHIs) in preventive care have yet to be systematically evaluated.ObjectiveThis review aimed to identify and describe the scope and use of current DHIs for preventive care in primary care settings.MethodsA scoping review to identify literature published from 2014 to 2020 was conducted across multiple databases using keywords and Medical Subject Headings terms covering primary care professionals, prevention and care management, and digital health. A subgroup analysis identified relevant studies conducted in US primary care settings, excluding DHIs that use the electronic health record (EHR) as a retrospective data capture tool. Technology descriptions, outcomes (eg, health care performance and implementation science), and study quality as per Oxford levels of evidence were abstracted.ResultsThe search yielded 5274 citations, of which 1060 full-text articles were identified. Following a subgroup analysis, 241 articles met the inclusion criteria. Studies primarily examined DHIs among health information technologies, including EHRs (166/241, 68.9%), clinical decision support (88/241, 36.5%), telehealth (88/241, 36.5%), and multiple technologies (154/241, 63.9%). DHIs were predominantly used for tertiary prevention (131/241, 54.4%). Of the core primary care functions, comprehensiveness was addressed most frequently (213/241, 88.4%). DHI users were providers (205/241, 85.1%), patients (111/241, 46.1%), or multiple types (89/241, 36.9%). Reported outcomes were primarily clinical (179/241, 70.1%), and statistically significant improvements were common (192/241, 79.7%). Results were summarized across the following 5 topics for the most novel/distinct DHIs: population-centered, patient-centered, care access expansion, panel-centered (dashboarding), and application-driven DHIs. The quality of the included studies was moderate to low.ConclusionsPreventive DHIs in primary care settings demonstrated meaningful improvements in both clinical and nonclinical outcomes, and across user types; however, adoption and implementation in the US were limited primarily to EHR platforms, and users were mainly clinicians receiving alerts regarding care management for their patients. Evaluations of negative results, effects on health disparities, and many other gaps remain to be explored.

Integration of Machine Learning in Clinical Decision Support Systems

Clinical decision support (CDS) systems offer healthcare professionals real-time, evidence-based assistance for the diagnosis, treatment, and monitoring of medical disorders, which has the potential to enhance patient outcomes. The application of machine learning algorithms in CDS systems has increased the reach and precision of these systems, enabling the examination of intricate patient data and the discovery of as-yet-unrecognized connections and patterns. This review article focuses on machine learning algorithms' uses in diagnosis and disease categorization, treatment selection and optimization, and patient monitoring and prognosis in order to examine the advantages and drawbacks of utilizing them in CDS systems. It also looks at the moral and legal issues surrounding the use of these systems, such as privacy issues and responsibility for choices made utilizing CDS technologies. A discussion of potential paths and difficulties for applying machine learning algorithms in CDS systems finishes the review. The incorporation of real-time data streams, the creation of more understandable algorithms, and the inclusion of patient preferences and values in decision-making are all possible ways to enhance CDS systems using machine learning. The necessity for thorough validation and regulatory control, as well as worries about the possible impact on clinician-patient relationships, are obstacles and difficulties to widespread implementation in clinical practice. The potential advantages of applying machine learning algorithms to CDS systems are highlighted in this paper, but it also stresses the need to address moral and legal issues and make sure that these systems are used in a responsible and open manner.

Development and Implementation of Pediatric Nursing-Clinical Decision Support System for Hyperthermia: A Pre- and Post-Test.

Firstly, we form the Pediatric Nursing-Knowledge Base for Hyperthermia, which combines publicly clinical practice guidelines and nursing routines of hyperthermia management. Then, following the nursing process framework, the system is developed by clinical decision support technology. Finally, a pre- and post-test is adopted to examine the effectiveness, usability and feasibility before and after using the system. Its effectiveness is examined by nursing records quality including completeness of nursing assessment, timeliness of nursing diagnosis, individualization of nursing interventions, and timeliness of nursing evaluation. Its usability and feasibility are assessed using the Clinical Nursing Information System Effectiveness Evaluation Scale. There is a significant difference between the two groups in effectiveness, usability and feasibility. Although the system is developed specifically for our hospital workflow and processes, the Pediatric Nursing-Knowledge Base for Hyperthermia and workflow for hyperthermia management in this study can be used as a reference to other hospitals.

Usability barriers and facilitators of a human factors engineering-based clinical decision support technology for diagnosing pulmonary embolism

BackgroundHealth IT, such as clinical decision support (CDS), has the potential to improve patient safety. However, poor usability of health IT continues to be a major concern. Human factors engineering (HFE) approaches are recommended to improve the usability of health IT. Limited evidence exists on the actual impact of HFE methods and principles on the usability of health IT. ObjectiveTo identify and describe the usability barriers and facilitators of an HFE-based CDS prior to implementation in the emergency department (ED). MethodsWe conducted debrief interviews with 32 emergency medicine physicians as a part of a scenario-based simulation study evaluating the usability of the HFE-based CDS. We performed a deductive content analysis of the interviews using the usability criteria of Scapin and Bastien as a framework. ResultsWe identified 271 occurrences of usability barriers (94) and facilitators (177) of the HFE-based CDS. For instance, we found a facilitator relating to the usability criteria prompting as the PE Dx helps the physician order diagnostic tests following the risk assessment. We found the most facilitators relating to the criteria, minimal actions, e.g. as the PE Dx automatically populates vitals signs (e.g., heart rate) from the chart into the CDS. The majority of the usability barriers related to the usability criteria, compatibility (i.e., workflow integration), which was not explicitly considered in the HFE design of the CDS. For example, the CDS did not support resident and attending physician teamwork in the PE diagnostic process. ConclusionThe systematic use of HFE principles in the design of CDS improves the usability of these technologies. In order to further reduce usability barriers, workflow integration should be explicitly considered in the design of health IT.

Development and Implementation of a Pediatric Nursing-Clinical Decision Support System for Hyperthermia: A Pre- and Post-test.

This article describes the development process and application of the Pediatric Nursing-Clinical Decision Support System for Hyperthermia. Firstly, we formed the Pediatric Nursing-Knowledge Base for Hyperthermia, which combines publicly available clinical practice guidelines and nursing routines of hyperthermia management. Then, following the nursing process framework, the system was developed using clinical decision support technology. Finally, a pre- and post-test were adopted to examine the effectiveness, usability, and feasibility before (1st to 31st of August 2018) and after (1st to 31st of December 2019) using the system. Its effectiveness was examined by analysis of nursing records' quality, including completeness of nursing assessment, timeliness of nursing diagnosis, individualization of nursing interventions, and timeliness of nursing evaluation. Its usability and feasibility were assessed using the Clinical Nursing Information System Effectiveness Evaluation Scale. There was a significant difference between the two groups in effectiveness, usability, and feasibility. Although the system was developed specifically for our hospital workflow and processes, the Pediatric Nursing-Knowledge Base for Hyperthermia and workflow for hyperthermia management in this study can be used as a reference to other hospitals.

A Study to Predict Response to Azacitidine or Lenalidomide/Azacitidine Combination Therapy in MDS and AML Patients Using a Computational Biological Modeling (CBM) Approach

Background: Several treatment options are available for patients with myelodysplastic syndromes (MDS) or acute myeloid leukemia (AML); however, the majority of patients will fail treatment or relapse. Predicting response is imperative in determining the right treatment regimen for each patient but currently no validated method exists.Aim: In this study, we aimed to predict treatment response in patients with MDS or AML using Cellworks' computational biology modeling (CBM) approach.Methods: In total, 16 patients were included in this analysis. 11 patients were treated on a single-institution phase II study of 5-day intravenous azacitidine (AZA) in patients with previously untreated MDS (Martin et al, AM J Hematol 2009 [NCT00384956]). An additional 5 patients were treated on a single-institution phase I study of daily high-dose lenalidomide (LEN) and 5-day intravenous AZA induction followed by low dose LEN and AZA maintenance in patients with newly diagnosed or relapsed AML (Ramsingh et al, Leukemia 2013 [NCT01016600]). Both treatments were generally well tolerated and showed promising response rates.For this analysis, whole exome sequencing (SureSelectXT Clinical Research Exome) and CGH microarrays were performed on previously collected samples associated with the respective clinical trials. The genomic data and non-genomic clinical data (age, diagnosis, white blood cell count, hemoglobin, platelet count, and bone marrow blast percentage) were used for CBM and response prediction. Digital drug simulations were conducted by quantitatively measuring drug effect on a cell growth score (proliferation + viability + apoptosis) along with impact on a identified disease specific biomarker unique to each patient. Each patient-specific protein network was screened for the extent by which AZA or LEN+AZA, respectively, reduced disease growth. The CBM tool used simulation prediction and clinical data through weighted decision tree analysis for the final response call. The predicted response was compared with the clinical response to assess for accuracy and predictive value. Patients who had a partial response or better per standard IMW criteria were classified as responders, all others were classified as non-responders.Results: 16 drug predictions were made by the CBM with 81% accuracy, 86% positive predictive value, 78% negative predictive value, 75% sensitivity and 88% specificity.In the subset of MDS patients treated with AZA, 4/11(36%) were classified as responders and 7/11 (64%) non-responders per IMW criteria. The median age at study entry was 73 and 6/11 were male. The median number of cycles of AZA administered was 6 (range 1-6). The CBM accurately predicted the clinical outcomes from 9/11 patients including 2/4 responders and 7/7 non-responders.In the subset of AML patients treated with LEN + AZA, 4/5 (80%) were classified as responders and 1/5 (20%) non-responders per IMW criteria. The median age at study entry was 71 and 1/5 were male. 3 of the patients had newly diagnosed AML and 2 had relapsed disease. The median number of cycles of LEN + AZA administered was 5 (range 1-17). The CBM accurately predicted the clinical outcomes from 4/5 patients including 4/4 responders.Conclusions: The CBM tested herein, which included genomic abnormalities and clinical data, showed high predictive value of protein network aberrations and clinical outcomes. The study validates the approach to a priori predict response and identify the right therapy option for the patient and could be used to establish eligibility criteria for precision enrollment in drug development trials. In addition, the network method uncovered possible molecular reasons for drug failure and resistance pathways that could be targeted to induce chemo sensitivity. This clinical decision support technology could enable improved effectiveness of therapy and help avoid unneeded toxicity and reduce health care expenses by eliminating unnecessary treatments. DisclosuresVij:Bristol-Meyers-Squibb: Honoraria; Takeda: Honoraria, Research Funding; Celgene: Honoraria; Abbvie: Honoraria; Amgen: Honoraria, Research Funding; Janssen: Honoraria; Konypharma: Honoraria; Jazz: Honoraria. Singh:Cellworks Research India Pvt. Ltd: Employment. Sahu:Cellworks Research India Pvt. Ltd: Employment. Kumari:Cellworks Research India Pvt. Ltd: Employment. Narayan:Cellworks Research India Pvt. Ltd: Employment. G:Cellworks Research India Pvt. Ltd: Employment. Vali:Cellworks Group Inc.: Employment. Abbasi:Cellworks Group Inc.: Employment.

E-Health Decision Support Technologies in the Prevention and Management of Pressure Ulcers: A Systematic Review.

Pressure ulcers are problematic across clinical settings, negatively impacting patient morbidity and mortality while resulting in substantial costs to the healthcare system. E-health clinical decision support technologies can play a key role in improving pressure ulcer-related outcomes. This systematic review aimed to assess the impact of electronic health decision support interventions on pressure ulcer management and prevention. A systematic search was conducted in PubMed, Scopus, Cumulative Index to Nursing and Allied Health Literature, and Cochrane. Nineteen articles, published from 2010 to 2020, were included for review. The findings of this review showed promising results regarding the usability and accuracy of electronic health decision support tools to aid in pressure ulcer prevention and management. Evidence indicated improved clinician adherence to pressure ulcer prevention practices and decreased healthcare costs postimplementation of an electronic health decision support intervention. However, the studies included in this review did not consistently show reductions in pressure ulcer prevalence, incidence, or risk. Most of the articles included in the review were limited by small sample sizes drawn from single hospitals or long-term care homes. More high-quality studies are needed to determine the types of electronic health decision support tools that can drive sustainable improvements to patient outcomes.

Evaluation of multiple myeloma clinical decision support tool value for assessing treatment selection variation when populated with community health system data.

e18781 Background: Clinical decision support (CDS) technology has the potential to improve health outcomes by offering physicians an informational resource to support review and application of best practices. The Multiple Myeloma Research Foundation (MMRF) and Intermountain Healthcare (IMH) conducted a study to assess the suitability of a single health system’s data for a myeloma-specific CDS tool that visualizes treatment pathways, and to assess the effort needed to support a CDS program. This research is part of a longer-term effort to explore how CDS technology can help: Increase awareness of and apply treatment guidelines by visualizing pathways for specific MM patient cohorts, Improve understanding of treatment variation for quality improvement within healthcare systems, Improve outcomes research by visualizing relationships between treatments and outcomes. This abstract focuses on the second use case, showing suitability of community health system data to assess treatment variability within the health system. Methods: IA12 data from the CoMMpass study was used to create a CDS tool prototype. These data were aggregated into state and transition maps to identify nodes and pathways with corresponding outcomes, including response, progression-free survival (PFS), and overall survival (OS). We also tested if EMR data from a community health system (i.e., IMH) could support such visualization. Inclusion criteria included patients with active MM between January 2016–June 2018; adult aged 18 years to 89 years at diagnosis of active or smoldering MM. An IMH-specific data dictionary was assessed for variable importance, quantity, and ease of acquisition. Results: Ninety-six of an initial 146 patients meeting eligibility criteria had sufficient data usable for the study, reflecting 44 unique drug combinations across 9 lines of therapy. The tool was able to associate and visualize all patients and their clinical states and transitions to their outcomes. Clinical data was typically complete (99% of the time), including key clinician-derived data, such as ECOG scores (78%) and treatment response (99%). Conclusions: The IMH portion of the study supports the hypothesis that a community health system can provide sufficiently high-quality information to power a CDS tool with priority features including display of treatment selection variability. Only 65% (96/146) of the initial study group had usable data because some patients had received partial care outside of the IMH integrated delivery network (IDN) leaving associated data inaccessible. Initial biostatistical analysis suggests that roughly 750-1000 complete patient records would be required for statistically significant outcomes research with granularly stratified cohorts. The MMRF plans to recruit additional large IDNs to obtain the patients to power more generalizable functionality.

Abstract PO-095: The PROState AI Cancer–Decision Support (PROSAIC-DS) pilot study: Clinical decision support technology and its role in prostate cancer MDT meetings

Abstract Background Multidisciplinary teams (MDT/tumour boards) were first introduced in the 1990s and have experienced little change to their methodology since. Universally used for the treatment of prostate cancer (CaP) in the UK new interventions are proposed to improve MDT efficiency and patient outcomes. Clinical practice guidelines (CPGs), designed to increase the uptake of evidence-based practice, suffer from lack of proper implementation. There is increasing evidence to suggest a gap between CPGs and actual treatment and the use of artificial intelligence an (AI) systems can help to increase the efficiency of the MDT. Methods We evaluated differences in MDT concordance with guidelines in the primary treatment of localised or locally advanced prostate cancer using the Deontics AI based custom clinical decision support software (CDSS) software. 59 paper cases were created by an expert clinician, 9 of which were excluded as they did not meet eligibility criteria. The remaining 50 cases were provided to the CDSS for evaluation. Simultaneously, two physicians assessed each patient case and provided their treatment recommendations. The results were assessed for concordance with UK, European and American guidelines, inter-rater reliability and trends in concordance based on patient variables.Results Overall clinician concordance with guidelines was 76%, while total concordance with all three guidelines was 28%. Overall concordance was highest with NICE guidelines, while total concordance was highest for NCCN guidelines. Inter-rater reliability was highest for the NCCN guidelines. Age &amp;lt; 75 (p &amp;lt;0.001; odds ratio [OR], 35.000), prostate volume &amp;lt; 46.5ml (p =0.047; OR, 4.909), and a Gleason score ≠ 8 (p =0.013; OR, 12.333), were all significantly associated with increased guideline concordance in this study. Conclusions Concordance with CPGs needs to be improved in specific patient groups. This may reflect cognitive bias, cognitive overload, or conflicting guideline recommendations and evidence base. One potential solution may be the integration of CDSS technology into the MDT setting. Citation Format: Vishal Santis, Deborah Enting, Vivek Patkar, Anastasia Chalkidou, John Fox, Danny Ruta, Jonathan K. Makanjuola. The PROState AI Cancer–Decision Support (PROSAIC-DS) pilot study: Clinical decision support technology and its role in prostate cancer MDT meetings [abstract]. In: Proceedings of the AACR Virtual Special Conference on Artificial Intelligence, Diagnosis, and Imaging; 2021 Jan 13-14. Philadelphia (PA): AACR; Clin Cancer Res 2021;27(5_Suppl):Abstract nr PO-095.

Current Challenges and Barriers to Real-World Artificial Intelligence Adoption for the Healthcare System, Provider, and the Patient.

Artificial intelligence (AI), or the use of automated systems that display the ability to correctly interpret, to learn from, and to achieve specific goals by use of external data, is an emerging technology that has myriad implications for changing the way we interact with the world. Although this technology is already being used in many fields such as banking, retail, and education, AI has the potential to transform other fields including healthcare. Within healthcare, ophthalmology is uniquely positioned to benefit from AI not only through clinical decision support technology but also through improved image processing innovations such as real-time segmentation, automated image quality improvements, and assisted or autonomous disease screening tools.1,2 Although there are now Food and Drug Administration–approved technologies within ophthalmology, such as IDx-DR (Coralville, IA, USA) for early diagnosis of diabetic retinopathy and diabetic macular edema, numerous challenges still exist to realize the potentially transformative impact of these technologies in day to day practice. The need for the ophthalmology community to take a thoughtful approach to AI innovation and implementation is accentuated by the high stakes involved. The impact of misleading patients and clinicians on a health condition is much greater than a retail store misinterpreting the next book you may like to buy. As a result, we need to increase discussion about the issues surrounding who, what, when, how, and why we might use AI in practice, including ethical and liability considerations, to determine how best to implement AI for all stakeholders including practitioners, patients, practices/hospitals, and industry. This article aims to highlight the challenges and barriers to real-world AI adoption that impact the technology's utility. We examine these specific challenges that will face health care organizations, providers, and patients.

Rapid response to COVID-19: health informatics support for outbreak management in an academic health system.

ObjectiveTo describe the implementation of technological support important for optimizing clinical management of the COVID-19 pandemic.Materials and MethodsOur health system has confirmed prior and current cases of COVID-19. An Incident Command Center was established early in the crisis and helped identify electronic health record (EHR)-based tools to support clinical care.ResultsWe outline the design and implementation of EHR-based rapid screening processes, laboratory testing, clinical decision support, reporting tools, and patient-facing technology related to COVID-19.DiscussionThe EHR is a useful tool to enable rapid deployment of standardized processes. UC San Diego Health built multiple COVID-19-specific tools to support outbreak management, including scripted triaging, electronic check-in, standard ordering and documentation, secure messaging, real-time data analytics, and telemedicine capabilities. Challenges included the need to frequently adjust build to meet rapidly evolving requirements, communication, and adoption, and to coordinate the needs of multiple stakeholders while maintaining high-quality, prepandemic medical care.ConclusionThe EHR is an essential tool in supporting the clinical needs of a health system managing the COVID-19 pandemic.

Application of human factors to improve usability of clinical decision support for diagnostic decision-making: a scenario-based simulation study

ObjectiveIn this study, we used human factors (HF) methods and principles to design a clinical decision support (CDS) that provides cognitive support to the pulmonary embolism (PE) diagnostic decision-making process...

Scope and Influence of Electronic Health Record–Integrated Clinical Decision Support in the Emergency Department: A Systematic Review

As electronic health records evolve, integration of computerized clinical decision support offers the promise of sorting, collecting, and presenting this information to improve patient care. We conducted a systematic review to examine the scope and influence of electronic health record-integrated clinical decision support technologies implemented in the emergency department (ED). A literature search was conducted in 4 databases from their inception through January 18, 2018: PubMed, Scopus, the Cumulative Index of Nursing and Allied Health, and Cochrane Central. Studies were included if they examined the effect of a decision support intervention that was implemented in a comprehensive electronic health record in the ED setting. Standardized data collection forms were developed and used to abstract study information and assess risk of bias. A total of 2,558 potential studies were identified after removal of duplicates. Of these, 42 met inclusion criteria. Common targets for clinical decision support intervention included medication and radiology ordering practices, as well as more comprehensive systems supporting diagnosis and treatment for specific disease entities. The majority of studies (83%) reported positive effects on outcomes studied. Most studies (76%) used a pre-post experimental design, with only 3 (7%) randomized controlled trials. Numerous studies suggest that clinical decision support interventions are effective in changing physician practice with respect to process outcomes such as guideline adherence; however, many studies are small and poorly controlled. Future studies should consider the inclusion of more specific information in regard to design choices, attempt to improve on uncontrolled before-after designs, and focus on clinically relevant outcomes wherever possible.

WT1 and BCORL1 Identified By Computational Biology Modeling Analysis of Patient Genomics Are Novel Predictors of Response to Azacitidine (AZA) and Lenalidomide (LEN) Treatment in Acute Myeloid Leukemia (AML)

Introduction: AML patients with relapsed/refractory (R/R) disease have few effective treatment options. LEN+AZA may be an active and better tolerated regimen compared with conventional chemotherapy. This combination has been tested in a phase 2 pilot study of LEN+AZA in 37 R/R older patients with a 49% overall response rate (4 complete remission (CR) / CR with incomplete recovery (CRi) and 14 with morphologic leukemia free state (MLFS). The objective of this study was to utilize the genomics of these patients to retrospectively predict response to AZA+LEN treatment using CBM workflow and identify key genomic characteristics of the responder profiles.Methods: We analyzed the clinical and genomic (NGS WES, cytogenetics) data from pre-treatment sample for a cohort of R/R AML patients. (Blood 2017 130:1337) All available genomic data was input into the CBM predictive workflow (Cellworks Group). Customized, individualized AML patient models - patient specific disease network maps of activated and inactivated dysregulated pathways were created.Digital drug models of AZA and LEN were created for CBM by programming their mechanism of action and effects on specific protein targets and pathways determined from published literature. The digital drug models were simulated individually and in combination on every patient's disease at varying doses. Drug impact was assessed by quantitatively measuring a disease inhibition score (DIS), the degree to which cancer phenotypes (proliferation, viability, apoptosis) were repressed, along with impact on the identified patient specific biomarkers. CBM predictions were compared with clinical outcomes in a retrospective blinded manner. Clinical response was defined as partial response or better as per International Working Group 2006 criteria.Results: 32 of 37 patients were analyzed by CBM based on availability of genomic profiling. 24 of the 32 patients were clinically evaluated, of which 4 had CR or CRi and 9 had MLFS response. CBM predictions matched for 20 of 24 profiles. The statistics of prediction accuracy against clinical outcome are presented in Table 1.A common aberration associating with sensitivity discovered in 7 profiles was a synonymous WT1 SNP rs16754. It was found in 1 CR and 5 MLFS cases and in the 1 non-responder case that along with WT1 SNP also had mutations in EZH2 and NOTCH1 that caused drug resistance. This WT1 SNP rs16754 correlated with higher mRNA expression of Wt1 and was associated with significantly improved outcome in pAML (PMID: 21189390) WT1 directly transactivates DNMT3A expression, thus improving response to hypomethylating agent. (PMID: 23042785)Another surprising find was the LOF mutation in BCORL1 (BCL6 Corepressor Like 1) L622P, that was the only common aberration found in all the 4 CR profiles. BCORL1 is a transcriptional corepressor that can also bind and regulate class II HDACs. LOF mutations in BCORL1 have been found in AML patients with abnormal karyotypes (PMID:22210327) In aplastic anemia, clones with BCORL1 mutations correlated with better response to immunosuppressive therapy and higher OS (PMID: 26132940), but no such association has been established in AML and may be worth exploring further in larger datasets.Conclusion: The CBM prediction of AZA+LEN in R/R AML patients based on genomic abnormalities and clinical data showed high predictive accuracy of 83.33%. The study validates the approach to a priori predict response and identify the right therapy option for the patient and could be used to establish criteria for precision enrollment in drug development trials. In addition, analysis uncovered novel predictors of response to AZA+LEN therapy that include presence of WT1 SNP rs16754 and possibly BCORL1 LOF, that were found in the responder profiles and linked to enhancing the drug efficacy network. This clinical decision support technology and these biomarkers may improve patient selection for AZA+LEN treatment in R/R AML. [Display omitted] DisclosuresAbbasi:Cell Works Group Inc.: Employment. Singh:Cellworks Research India Private Limited: Employment. Ullal:Cellworks Research India Private Limited: Employment. Kumari:Cellworks Research India Private Limited: Employment. Tyagi:Cellworks Research India Private Limited: Employment. Alam:Cellworks Research India Private Limited: Employment. Lunkad:Cellworks Research India Private Limited: Employment. Cogle:Celgene: Other: Steering Committee Member of Connect MDS/AML Registry. Vali:Cell Works Group Inc.: Employment. Pollyea:AbbVie: Consultancy, Research Funding; Celgene: Membership on an entity's Board of Directors or advisory committees; Karyopharm: Membership on an entity's Board of Directors or advisory committees; Curis: Membership on an entity's Board of Directors or advisory committees; Gilead: Consultancy; Pfizer: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Celyad: Consultancy, Membership on an entity's Board of Directors or advisory committees; Argenx: Consultancy, Membership on an entity's Board of Directors or advisory committees; Agios: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding.