scholarly journals Design and Implementation of a Guideline-Based Workflow Software System for Improving the Chemotherapy Process

2021 ◽  
Vol In Press (In Press) ◽  
Author(s):  
Mohammad Reza Afrash ◽  
Azamossadat Hosseini ◽  
Reza Rabiei ◽  
Sina Salari ◽  
Mohammad Mehdi Sepehri ◽  
...  
Keyword(s):  
Healthcare Providers ◽  
Clinical Decision ◽  
Nursing Administration ◽  
Safety Improvement ◽  
Specific Standard ◽  
Computer Based ◽  
Communication And Coordination ◽  
Minimum Dataset ◽  
Python Programming ◽  
Based Medicine

Background: While chemotherapy is an effective modality for the treatment of patients with cancer, it is a complex, multidisciplinary, and error-prone process. Paper-based protocols are commonly applied in chemotherapy; however, they fail to eliminate the complexity of this process. Therefore, a new guideline-based workflow software (GWS) system is needed to improve the workflow and quality of chemotherapy process. Methods: Planning was initiated 11 months before the system implementation and it involved a multidisciplinary group to analyze the current chemotherapy workflow and protocols for identifying the workflow components, analyzing paper-based protocols, developing computer-based protocols, and designing of systems based on an object-oriented analysis. To implement the GWS, we applied a system based on Python programming language and SQL language. Results: The conceptual model was developed based on need assessments and chemotherapy steps. A minimum dataset was developed for the electronic health records. We established examination forms for the patient management system (PMS), as well as specific standard forms for chemotherapy ordering, prescription verification and administration templates. Finally, developed GWS system consisted of a PMS, computerized provider order entry (CPOE), prescription verification system (PVS), and nursing administration system (NAS). Conclusions: A PMS, a PVS, a NAS, and a protocol-based clinical decision support system were integrated into the CPOE system to improve the chemotherapy process. Elimination of iterations and unnecessary steps in old chemotherapy workflow, increase of patient safety, improvement of communication and coordination between healthcare providers, and use of updated evidence-based medicine in direct chemotherapy orders justify the integration of GWS in the cancer care settings.

Supporting Knowledge-Based Decision Making in the Medical Context

2011 ◽  
Vol 1 (1) ◽  
pp. 42-60 ◽  
Author(s):  
Luca Anselma ◽  
Alessio Bottrighi ◽  
Gianpaolo Molino ◽  
Stefania Montani ◽  
Paolo Terenziani ◽  
...  
Keyword(s):  
Decision Making ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Medical Decision ◽  
Theory Analysis ◽  
Knowledge Based ◽  
Therapeutic Decisions ◽  
Computer Based ◽  
Based Medicine ◽  
Domain Independent

Knowledge-based clinical decision making is one of the most challenging activities of physicians. Clinical Practice Guidelines are commonly recognized as a useful tool to help physicians in such activities by encoding the indications provided by evidence-based medicine. Computer-based approaches can provide useful facilities to put guidelines into practice and to support physicians in decision-making. Specifically, GLARE (GuideLine Acquisition, Representation and Execution) is a domain-independent prototypical tool providing advanced Artificial Intelligence techniques to support medical decision making, including what-if analysis, temporal reasoning, and decision theory analysis. The paper describes such facilities considering a real-world running example and focusing on the treatment of therapeutic decisions.

Guideline Representation Ontologies for Evidence-Based Medicine Practice

2010 ◽  
pp. 234-254 ◽  
Author(s):  
Kai Zheng ◽  
Rema Padman ◽  
Michael P. Johnson ◽  
Sharique Hasan
Keyword(s):  
Clinical Practice ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Decision Criterion ◽  
Evidence Based ◽  
Medical Decisions ◽  
Computer Based ◽  
Interchange Format ◽  
Key Steps ◽  
Based Medicine

An ontology in the context of guideline representation is a specification of conceptualizations that constitutes evidence-based clinical practice guidelines. It represents the elements of a guideline by specifying its attributes and defining the relationships that hold among them. For example, a guideline representation ontology would define a set of medical decisions and actions (concepts), as well as a set of rules (relationships) that relate the evaluation of a decision criterion to further reasoning steps or to its associated actions. A rigorously defined computational ontology provides considerable promise of producing computable representations that can be visualized, edited, executed, and shared using computer-based systems. A widely acknowledged ontology, or standard representation schema, is the key to facilitating the dissemination of guidelines across computer systems and healthcare institutions. The first part of this chapter presents the evolution of ontology research in guideline representation. Several representative ontologies are reviewed and discussed, with in-depth analyses of two popular models: GLIF (Guideline Interchange Format) and PROforma. The second part of the chapter analyzes seven key elements constituting a guideline representation. It also discusses the criteria for evaluating competing ontologies and some known limitations in the existing models. At the end of this chapter, four key steps are outlined that converts a guideline into computerized representation, which can be then used in Clinical Decision Support Systems (CDSSs).

Supporting Knowledge-Based Decision Making in the Medical Context

2011 ◽  
pp. 1721-1737
Author(s):  
Luca Anselma ◽  
Alessio Bottrighi ◽  
Gianpaolo Molino ◽  
Stefania Montani ◽  
Paolo Terenziani ◽  
...  
Keyword(s):  
Decision Making ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Medical Decision ◽  
Evidence Based ◽  
Knowledge Based ◽  
Therapeutic Decisions ◽  
Computer Based ◽  
Based Medicine ◽  
Domain Independent

Knowledge-based clinical decision making is one of the most challenging activities of physicians. Clinical Practice Guidelines are commonly recognized as a useful tool to help physicians in such activities by encoding the indications provided by evidence-based medicine. Computer-based approaches can provide useful facilities to put guidelines into practice and to support physicians in decision-making. Specifically, GLARE (GuideLine Acquisition, Representation and Execution) is a domain-independent prototypical tool providing advanced Artificial Intelligence techniques to support medical decision making, including what-if analysis, temporal reasoning, and decision theory analysis. The paper describes such facilities considering a real-world running example and focusing on the treatment of therapeutic decisions.

Supporting Knowledge-Based Decision Making in the Medical Context

2012 ◽  
pp. 24-42
Author(s):  
Luca Anselma ◽  
Alessio Bottrighi ◽  
Gianpaolo Molino ◽  
Stefania Montani ◽  
Paolo Terenziani ◽  
...  
Keyword(s):  
Decision Making ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Medical Decision ◽  
Evidence Based ◽  
Knowledge Based ◽  
Therapeutic Decisions ◽  
Computer Based ◽  
Based Medicine ◽  
Domain Independent

Knowledge-based clinical decision making is one of the most challenging activities of physicians. Clinical Practice Guidelines are commonly recognized as a useful tool to help physicians in such activities by encoding the indications provided by evidence-based medicine. Computer-based approaches can provide useful facilities to put guidelines into practice and to support physicians in decision-making. Specifically, GLARE (GuideLine Acquisition, Representation and Execution) is a domain-independent prototypical tool providing advanced Artificial Intelligence techniques to support medical decision making, including what-if analysis, temporal reasoning, and decision theory analysis. The paper describes such facilities considering a real-world running example and focusing on the treatment of therapeutic decisions.

scholarly journals Analyzing the Role of Data Mining in Design and Development of an EBM Enabled Open Source APMIS

2019 ◽  
Vol 8 (12S) ◽  
pp. 751-756
Keyword(s):  
Decision Making ◽  
Open Source ◽  
Open Source Software ◽  
Clinical Decision Making ◽  
Low Cost ◽  
Healthcare Providers ◽  
Clinical Decision ◽  
Management Information ◽  
Government Sector ◽  
Based Medicine

This research has investigated open source software solutions and clinical data to provide a low cost improved advanced pathology management information system [APMIS]. This APMIS facilitate the Evidence Based Medicine (EBM) to provide accurate and error-free diagnosis. In the most of developing countries healthcare is mainly government sector service, due to the limited available resources most of the hospitals are lacking in providing the best services at time. Finance is one of complex issues in the development of an exhaustive healthcare system. Open source software solutions can be proved as a best alternative for achieving the required services at very low cost. While treating a patient any poor clinical decision is unacceptable as it can lead towards a disastrous situation where life of a patient is on stake. Healthcare providers must go for employment of computerized management information (MIS) and/or IT based decision support systems (DSS).These systems use to generate huge amount of data. A hidden wealth of information use to be available with these data and it can be very supportive in process of clinical decision making. How we can support decision making in healthcare by extracting and utilizing useful information from these data is one of the main themes of this research.

scholarly journals Understanding cause of stillbirth: a prospective observational multi-country study from sub-Saharan Africa

2019 ◽  
Vol 19 (1) ◽  
Author(s):  
Mamuda Aminu ◽  
Sarah Bar-Zeev ◽  
Sarah White ◽  
Matthews Mathai ◽  
Nynke van den Broek
Keyword(s):  
Sierra Leone ◽  
Cause Of Death ◽  
Expert Panel ◽  
Healthcare Providers ◽  
Sub Saharan Africa ◽  
Primary Data ◽  
Middle Income ◽  
Computer Based ◽  
Sub Saharan ◽  
Low And Middle Income

Abstract Background Every year, an estimated 2.6 million stillbirths occur worldwide, with up to 98% occurring in low- and middle-income countries (LMIC). There is a paucity of primary data on cause of stillbirth from LMIC, and particularly from sub-Saharan Africa to inform effective interventions. This study aimed to identify the cause of stillbirths in low- and middle-income settings and compare methods of assessment. Methods This was a prospective, observational study in 12 hospitals in Kenya, Malawi, Sierra Leone and Zimbabwe. Stillbirths (28 weeks or more) were reviewed to assign the cause of death by healthcare providers, an expert panel and by using computer-based algorithms. Agreement between the three methods was compared using Kappa (κ) analysis. Cause of stillbirth and level of agreement between the methods used to assign cause of death. Results One thousand five hundred sixty-three stillbirths were studied. The stillbirth rate (per 1000 births) was 20.3 in Malawi, 34.7 in Zimbabwe, 38.8 in Kenya and 118.1 in Sierra Leone. Half (50.7%) of all stillbirths occurred during the intrapartum period. Cause of death (range) overall varied by method of assessment and included: asphyxia (18.5–37.4%), placental disorders (8.4–15.1%), maternal hypertensive disorders (5.1–13.6%), infections (4.3–9.0%), cord problems (3.3–6.5%), and ruptured uterus due to obstructed labour (2.6–6.1%). Cause of stillbirth was unknown in 17.9–26.0% of cases. Moderate agreement was observed for cause of stillbirth as assigned by the expert panel and by hospital-based healthcare providers who conducted perinatal death review (κ = 0.69; p < 0.0005). There was only minimal agreement between expert panel review or healthcare provider review and computer-based algorithms (κ = 0.34; 0.31 respectively p < 0.0005). Conclusions For the majority of stillbirths, an underlying likely cause of death could be determined despite limited diagnostic capacity. In these settings, more diagnostic information is, however, needed to establish a more specific cause of death for the majority of stillbirths. Existing computer-based algorithms used to assign cause of death require revision.

DESIGN OF DECOUPLED CLINICAL DECISION SUPPORT FOR SERVICE-ORIENTED ARCHITECTURES

2009 ◽  
Vol 19 (02) ◽  
pp. 159-183 ◽  
Author(s):  
JENS WEBER-JAHNKE
Keyword(s):  
Decision Support ◽  
Clinical Decision Support ◽  
Service Oriented Architecture ◽  
Empirical Evaluation ◽  
Cost Savings ◽  
Clinical Decision ◽  
Service Oriented Architectures ◽  
Distributed Services ◽  
Service Oriented ◽  
Computer Based

Computer-based clinical decision support (CDS) contributes to cost savings, increased patient safety and quality of medical care. Most existing CDS systems are stand-alone products (first generation) or part of complete electronic medical record packages (second generation). Experience shows that creating and maintaining CDS systems is expensive and requires effort that should be economized by sharing them among multiple users. It makes good economic sense to share CDS service installations among a larger set of client systems. The paradigm of a service-oriented architecture (SOA) embraces this idea of sharing distributed services. Some attempts making CDS services available to distributed health information systems exist. However, these approaches have not gained much adoption. We argue that they do not provide a sufficient level of decoupling between client and CDS in order to be broadly reusable in SOAs. In this paper, we present a new CDS service component called EGADSS, which has been designed and implemented with the declared objective to minimize the coupling between client and CDS server. We present our key design decisions, which are guided by empirical research in SOA development. We evaluate our result theoretically by measuring the level of decoupling achieved compared to existing CDS approaches. Furthermore, we report on an empirical evaluation of the resulting design, integrating the EGADSS service with an example client system.

scholarly journals Updated Review of Blood Culture Contamination

2006 ◽  
Vol 19 (4) ◽  
pp. 788-802 ◽  
Author(s):  
Keri K. Hall ◽  
Jason A. Lyman
Keyword(s):  
Health Care ◽  
Decision Support ◽  
Blood Culture ◽  
Infection Control ◽  
Clinical Decision ◽  
Potential Utility ◽  
Diagnostic Uncertainty ◽  
Computer Based ◽  
Clinically Significant ◽  
Care Costs

SUMMARY Blood culture contamination represents an ongoing source of frustration for clinicians and microbiologists alike. Ambiguous culture results often lead to diagnostic uncertainty in clinical management and are associated with increased health care costs due to unnecessary treatment and testing. A variety of strategies have been investigated and employed to decrease contamination rates. In addition, numerous approaches to increase our ability to distinguish between clinically significant bacteremia and contamination have been explored. In recent years, there has been an increase in the application of computer-based tools to support infection control activities as well as provide clinical decision support related to the management of infectious diseases. Finally, new approaches for estimating bacteremia risk which have the potential to decrease unnecessary blood culture utilization have been developed and evaluated. In this review, we provide an overview of blood culture contamination and describe the potential utility of a variety of approaches to improve both detection and prevention. While it is clear that progress is being made, fundamental challenges remain.

scholarly journals The Benefits of Big Data Analytics in the Healthcare Sector

2015 ◽  
pp. 406-439 ◽  
Author(s):  
Andrea Darrel ◽  
Margee Hume ◽  
Timothy Hardie ◽  
Jeffery Soar
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Healthcare Providers ◽  
Big Data Analytics ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Healthcare Sector ◽  
Improved Outcomes ◽  
Health Initiatives ◽  
Remote Patient

The benefits of big data analytics in the healthcare sector are assumed to be substantial, and early proponents have been very enthusiastic (Chen, Chiang, & Storey, 2012), but little research has been carried out to confirm just what those benefits are, and to whom they accrue (Bollier, 2010). This chapter presents an overview of existing literature that demonstrates quantifiable, measurable benefits of big data analytics, confirmed by researchers across a variety of healthcare disciplines. The chapter examines aspects of clinical operations in healthcare including Cost Effectiveness Research (CER), Clinical Decision Support Systems (CDS), Remote Patient Monitoring (RPM), Personalized Medicine (PM), as well as several public health initiatives. This examination is in the context of searching for the benefits described resulting from the deployment of big data analytics. Results indicate the principle benefits are delivered in terms of improved outcomes for patients and lower costs for healthcare providers.

scholarly journals Factors Affecting Tendency and Adherence to Traditional Persian Medicine: A Qualitative Study

2021 ◽  
Vol In Press (In Press) ◽  
Author(s):  
Mansoureh Sabbagh-Bani-Azad ◽  
Roja Rahimi ◽  
Abbas Kebriaeezadeh ◽  
Mohammad Mahdi Zolfagharzadeh ◽  
Akbar Abdollahiasl
Keyword(s):  
Qualitative Study ◽  
Healthcare Providers ◽  
Modern Medicine ◽  
Saturation Point ◽  
Factors Affecting ◽  
Medical Discipline ◽  
Persian Medicine ◽  
Traditional Persian Medicine ◽  
Based Medicine ◽  
Depth Interviews

Background: Adherence to medications is crucial for them to be effective. Traditional Persian Medicine (TPM) is an ancient medical discipline originated from old Persia and is currently used along with modern medicine in Iran. Evaluating the factors affecting adherence to TPM can have far-reaching implications for policymakers to make informed decisions. Objectives: This qualitative study investigates the factors affecting tendency and adherence to TPM among Iranians. Methods: We collected data from stakeholders in TPM using a focus group involving 13 participants and by conducting four in-depth interviews. The saturation point was reached at the 4th interview. We recorded all the interviews and then transcribed them verbatim for thematic content analysis. Results: We obtained 297 codes and 29 sub-themes for the factors affecting adherence to TPM, including the factors affecting compliance and tendency. Then we extracted the main themes. People’s beliefs, the inherent characteristics of traditional medicine and its status quo, attempts to bring about positive changes to TPM, and the problems facing the modern health system were the main factors affecting adherence to TPM. Conclusions: Despite many strenuous efforts in Iran to study TPM along with the lines of evidence-based medicine, policymaking, financing, patients’ and healthcare providers’ education, popular beliefs, and administrative transparency are needed to be addressed more adequately to promote adherence to TPM and help build integrative medicine in Iran’s healthcare system.

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