Clinical decision support system to optimise symptom management in palliative medicine: focus group study

2021 ◽  
pp. bmjspcare-2021-002940
Author(s):  
Lotte van der Stap ◽  
Albert H de Heij ◽  
Agnes van der Heide ◽  
Anna KL Reyners ◽  
Yvette M van der Linden
Keyword(s):  
Palliative Care ◽  
Decision Support ◽  
Focus Group ◽  
Clinical Decision Support ◽  
Support System ◽  
Symptom Management ◽  
Clinical Decision Support System ◽  
Clinical Decision ◽  
Improve Symptom ◽  
Patient Reported

ObjectivesSuboptimal symptom control in patients with life-limiting illnesses is a major issue. A clinical decision support system (CDSS) that combines a patient-reported symptom assessment scale (SAS) and guideline-based individualised recommendations has the potential to improve symptom management. However, lacking end-user acceptance often prevents CDSS use in daily practice.We aimed to evaluate the acceptability and feasibility of a palliative care CDSS according to its targeted end-users.MethodsSix focus groups with different groups of stakeholders were conducted: (1) patient representatives; (2) community nurses; (3) hospital nurses; (4) general practitioners; (5) hospital physicians and (6) palliative care specialists. Audiotapes were transcribed verbatim and thematically analysed.ResultsFifty-one stakeholders (6–12 per focus group) participated. Six themes were discussed: effect, validity, continuity, practical usability, implementation and additional features. All participants expected a CDSS to improve symptom management, for example, by reminding clinicians of blind spots and prompting patient participation. They feared interference with professional autonomy of physicians, doubted the validity of using a patient-reported SAS as CDSS input and thought lacking care continuity would complicate CDSS use. Clinicians needed clear criteria for when to use the CDSS (eg, life-limiting illness, timing in illness trajectory). Participants preferred a patient-coordinated system but were simultaneously concerned patients may be unwilling or unable to fill out an SAS.ConclusionsA palliative care CDSS was considered useful for improving symptom management. To develop a feasible system, barriers for successful implementation must be addressed including concerns about using a patient-reported SAS, lacking care continuity and unclear indications for use.

scholarly journals Visualization of Similar Patients in a Clinical Decision Support System for Rare Diseases – A Focus Group Study

2021 ◽  
Author(s):  
Jannik Schaaf ◽  
Martin Sedlmayr ◽  
Hans-Ulrich Prokosch ◽  
Niels Tegtbauer ◽  
Dennis Kadioglu ◽  
...  
Keyword(s):  
Decision Support ◽  
Focus Group ◽  
Decision Support System ◽  
Clinical Decision Support ◽  
Rare Diseases ◽  
Support System ◽  
Clinical Decision Support System ◽  
Clinical Decision ◽  
On Demand ◽  
Group A

The diagnosis of patients with rare diseases is often delayed. A Clinical Decision Support System using similarity analysis of patient-based data may have the potential to support the diagnosis of patients with rare diseases. This qualitative study has the objective to investigate how the result of a patient similarity analysis should be presented to a physician to enable diagnosis support. We conducted a focus group with physicians practicing in rare diseases as well as medical informatics researchers. To prepare the focus group, a literature search was performed to check the current state of research regarding visualization of similar patients. We then created software-mockups for the presentation of these visualization methods for the discussion within the focus group. Two persons took independently field notes for data collection of the focus group. A questionnaire was distributed to the participants to rate the visualization methods. The results show that four visualization methods are promising for the visualization of similar patients: “Patient on demand table”, “Criteria selection”, “Time-Series chart” and “Patient timeline. “Patient on demand table” shows a direct comparison of patient characteristics, whereas “Criteria selection” allows the selection of different patient criteria to get deeper insights into the data. The “Time-Series chart” shows the time course of clinical parameters (e.g. blood pressure) whereas a “Patient timeline” indicates which time events exist for a patient (e.g. several symptoms on different dates). In the future, we will develop a software-prototype of the Clinical Decision Support System to include the visualization methods and evaluate the clinical usage.

scholarly journals Transforming User Needs into Functional Requirements for an Antibiotic Clinical Decision Support System

2013 ◽  
Vol 04 (04) ◽  
pp. 618-635 ◽  
Author(s):  
T.J. Bright
Keyword(s):  
Content Analysis ◽  
Decision Support ◽  
Focus Group ◽  
Clinical Decision Support ◽  
Support System ◽  
Clinical Decision Support System ◽  
Information Needs ◽  
Clinical Decision ◽  
Functional Requirements ◽  
Focus Group Study

SummaryBackground: Many informatics studies use content analysis to generate functional requirements for system development. Explication of this translational process from qualitative data to functional requirements can strengthen the understanding and scientific rigor when applying content analysis in informatics studies.Objectives: To describe a user-centered approach transforming emergent themes derived from focus group data into functional requirements for informatics solutions and to illustrate these methods to the development of an antibiotic clinical decision support system (CDS).Methods: The approach consisted of five steps: 1) identify unmet therapeutic planning information needs via Focus Group Study-I, 2) develop a coding framework of therapeutic planning themes to refine the domain scope to antibiotic therapeutic planning, 3) identify functional requirements of an antibiotic CDS system via Focus Group Study-II, 4) discover informatics solutions and functional requirements from coded data, and 5) determine the types of information needed to support the antibiotic CDS system and link with the identified informatics solutions and functional requirements.Results: The coding framework for Focus Group Study-I revealed unmet therapeutic planning needs. Twelve subthemes emerged and were clustered into four themes; analysis indicated a need for an antibiotic CDS intervention. Focus Group Study-II included five types of information needs. Comments from the Barrier/Challenge to information access and Function/Feature themes produced three informatics solutions and 13 functional requirements of an antibiotic CDS system. Comments from the Patient, Institution, and Domain themes generated required data elements for each informatics solution.Conclusions: This study presents one example explicating content analysis of focus group data and the analysis process to functional requirements from narrative data. Illustration of this 5-step method was used to develop an antibiotic CDS system, resolving unmet antibiotic prescribing needs. As a reusable approach, these techniques can be refined and applied to resolve unmet information needs with informatics interventions in additional domains.Citation: Bright TJ. Transforming user needs into functional requirements for an antibiotic clinical decision support system: explicating content analysis for system design. Appl Clin Inf 2013; 4: 618–635http://dx.doi.org/10.4338/ACI-2013-08-RA-0058

1309-P: Utilizing a Clinical Decision Support System for Hypoglycemia Management

Diabetes ◽  
2019 ◽  
Vol 68 (Supplement 1) ◽  
pp. 1309-P
Author(s):  
JACQUELYN R. GIBBS ◽  
KIMBERLY BERGER ◽  
MERCEDES FALCIGLIA
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Clinical Decision Support ◽  
Support System ◽  
Clinical Decision Support System ◽  
Clinical Decision

A Clinical Decision Support System for Assessing the Risk of Cardiovascular Diseases in Diabetic Hemodialysis Patients

2020 ◽  
Vol 16 (3) ◽  
pp. 262-269
Author(s):  
Tahere Talebi Azad Boni ◽  
Haleh Ayatollahi ◽  
Mostafa Langarizadeh
Keyword(s):  
Decision Support ◽  
Cardiovascular Diseases ◽  
Decision Support System ◽  
Clinical Decision Support ◽  
Support System ◽  
Clinical Decision Support System ◽  
Clinical Decision ◽  
Diabetic Patients ◽  
K Value ◽  
Care Plans

Background: One of the greatest challenges in the field of medicine is the increasing burden of chronic diseases, such as diabetes. Diabetes may cause several complications, such as kidney failure which is followed by hemodialysis and an increasing risk of cardiovascular diseases. Objective: The purpose of this research was to develop a clinical decision support system for assessing the risk of cardiovascular diseases in diabetic patients undergoing hemodialysis by using a fuzzy logic approach. Methods: This study was conducted in 2018. Initially, the views of physicians on the importance of assessment parameters were determined by using a questionnaire. The face and content validity of the questionnaire was approved by the experts in the field of medicine. The reliability of the questionnaire was calculated by using the test-retest method (r = 0.89). This system was designed and implemented by using MATLAB software. Then, it was evaluated by using the medical records of diabetic patients undergoing hemodialysis (n=208). Results: According to the physicians' point of view, the most important parameters for assessing the risk of cardiovascular diseases were glomerular filtration, duration of diabetes, age, blood pressure, type of diabetes, body mass index, smoking, and C reactive protein. The system was designed and the evaluation results showed that the values of sensitivity, accuracy, and validity were 85%, 92% and 90%, respectively. The K-value was 0.62. Conclusion: The results of the system were largely similar to the patients’ records and showed that the designed system can be used to help physicians to assess the risk of cardiovascular diseases and to improve the quality of care services for diabetic patients undergoing hemodialysis. By predicting the risk of the disease and classifying patients in different risk groups, it is possible to provide them with better care plans.

Massive transfusion experience, current practice and decision support: A survey of Australian and New Zealand anaesthetists

2021 ◽  
pp. 0310057X2097403
Author(s):  
Brenton J Sanderson ◽  
Jeremy D Field ◽  
Lise J Estcourt ◽  
Erica M Wood ◽  
Enrico W Coiera
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Clinical Decision Support ◽  
Support System ◽  
Clinical Decision Support System ◽  
Response Rate ◽  
Massive Transfusion ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Transfusion Practice

Massive transfusions guided by massive transfusion protocols are commonly used to manage critical bleeding, when the patient is at significant risk of morbidity and mortality, and multiple timely decisions must be made by clinicians. Clinical decision support systems are increasingly used to provide patient-specific recommendations by comparing patient information to a knowledge base, and have been shown to improve patient outcomes. To investigate current massive transfusion practice and the experiences and attitudes of anaesthetists towards massive transfusion and clinical decision support systems, we anonymously surveyed 1000 anaesthetists and anaesthesia trainees across Australia and New Zealand. A total of 228 surveys (23.6%) were successfully completed and 227 were analysed for a 23.3% response rate. Most respondents were involved in massive transfusions infrequently (88.1% managed five or fewer massive transfusion protocols per year) and worked at hospitals which have massive transfusion protocols (89.4%). Massive transfusion management was predominantly limited by timely access to point-of-care coagulation assessment and by competition with other tasks, with trainees reporting more significant limitations compared to specialists. The majority of respondents reported that they were likely, or very likely, both to use (73.1%) and to trust (85%) a clinical decision support system for massive transfusions, with no significant difference between anaesthesia trainees and specialists ( P = 0.375 and P = 0.73, respectively). While the response rate to our survey was poor, there was still a wide range of massive transfusion experience among respondents, with multiple subjective factors identified limiting massive transfusion practice. We identified several potential design features and barriers to implementation to assist with the future development of a clinical decision support system for massive transfusion, and overall wide support for a clinical decision support system for massive transfusion among respondents.

scholarly journals An Evolutionary Belief Rule-Based Clinical Decision Support System to Predict COVID-19 Severity under Uncertainty

2021 ◽  
Vol 11 (13) ◽  
pp. 5810
Author(s):  
Faisal Ahmed ◽  
Mohammad Shahadat Hossain ◽  
Raihan Ul Islam ◽  
Karl Andersson
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Clinical Decision Support ◽  
Support System ◽  
Clinical Decision Support System ◽  
Clinical Decision ◽  
Experimental Result ◽  
Rule Base ◽  
Rule Based ◽  
Belief Rule Base

Accurate and rapid identification of the severe and non-severe COVID-19 patients is necessary for reducing the risk of overloading the hospitals, effective hospital resource utilization, and minimizing the mortality rate in the pandemic. A conjunctive belief rule-based clinical decision support system is proposed in this paper to identify critical and non-critical COVID-19 patients in hospitals using only three blood test markers. The experts’ knowledge of COVID-19 is encoded in the form of belief rules in the proposed method. To fine-tune the initial belief rules provided by COVID-19 experts using the real patient’s data, a modified differential evolution algorithm that can solve the constraint optimization problem of the belief rule base is also proposed in this paper. Several experiments are performed using 485 COVID-19 patients’ data to evaluate the effectiveness of the proposed system. Experimental result shows that, after optimization, the conjunctive belief rule-based system achieved the accuracy, sensitivity, and specificity of 0.954, 0.923, and 0.959, respectively, while for disjunctive belief rule base, they are 0.927, 0.769, and 0.948. Moreover, with a 98.85% AUC value, our proposed method shows superior performance than the four traditional machine learning algorithms: LR, SVM, DT, and ANN. All these results validate the effectiveness of our proposed method. The proposed system will help the hospital authorities to identify severe and non-severe COVID-19 patients and adopt optimal treatment plans in pandemic situations.

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