CorRECTreatment: a web-based decision support tool for rectal cancer treatment that uses the analytic hierarchy process and decision tree

Multi-criteria decision analysis method for differential diagnosis of tropical febrile diseases

This paper employs the Analytical Hierarchy Process (AHP) to enhance the accuracy of differential diagnosis for febrile diseases, particularly prevalent in tropical regions where misdiagnosis may have severe consequences. The migration of health workers from developing countries has resulted in frontline health workers (FHWs) using inadequate protocols for the diagnosis of complex health conditions. The study introduces an innovative AHP-based Medical Decision Support System (MDSS) incorporating disease risk factors derived from physicians' experiential knowledge to address this challenge. The system's aggregate diagnostic factor index determines the likelihood of febrile illnesses. Compared to existing literature, AHP models with risk factors demonstrate superior prediction accuracy, closely aligning with physicians' suspected diagnoses. The model's accuracy ranges from 85.4% to 96.9% for various diseases, surpassing physicians' predictions for Lassa, Dengue, and Yellow Fevers. The MDSS is recommended for use by FHWs in communities lacking medical experts, facilitating timely and precise diagnoses, efficient application of diagnostic test kits, and reducing overhead expenses for administrators.

Pain Treatment Evaluation in COVID-19 Patients with Hesitant Fuzzy Linguistic Multicriteria Decision-Making

The coronavirus disease 2019 (COVID-19) has emerged as a worldwide pandemic since March 2020. Although most patients complain of moderate or severe pain, these symptoms are generally underestimated and appropriate treatment is not applied. This study aims to guide physicians in selecting and ranking various alternatives for the treatment of pain in COVID-19 patients. However, the choice of treatment for pain requires the consideration of many different conflicting criteria. Therefore, we have studied this problem as a multicriteria decision-making problem. Throughout the solution procedure, first, the criteria and subcriteria affecting the preferences are defined. Then, weight values are determined with respect to these criteria, as they have different degrees of importance for the problem. At this stage, hesitant fuzzy linguistic term sets (HFLTSs) are used, and thus, experts can convey their ideas more accurately. In this first phase of the study, an HFLTS integrated Analytic Hierarchy Process (AHP) method is utilized. Subsequently, possible treatment alternatives are evaluated by using the Vise Kriterijumska Optimizacija I Kompromisno Resenje (VIKOR) method. According to the results obtained by considering expert evaluations, the most preferred treatment is the administration of paracetamol, followed by interventional treatments, opioids, and nonsteroidal anti-inflammatory drugs (NSAIDs), respectively. With this study, it is ensured that a more accurate method is followed by eliminating possible mistakes due to the subjective evaluations of experts in the process of determining pain treatment. This method can also be used in different patient and disease groups.

Mapping Patient Data to Colorectal Cancer Clinical Algorithms for Personalized Guideline-Based Treatment

BACKGROUND

Colorectal cancer is the most commonly occurring cancer in Germany, and the second and third most commonly diagnosed cancer in women and men, respectively. In this context, evidence-based guidelines positively impact the quality of treatment processes for cancer patients. However, evidence of their impact on real-world patient care remains unclear. To ensure the success of clinical guidelines, a fast and clear provision of knowledge at the point of care is essential.

OBJECTIVES

The objectives of this study are to model machine-readable clinical algorithms for colon carcinoma and rectal carcinoma annotated by Unified Medical Language System (UMLS) based on clinical guidelines and the development of an open-source workflow system for mapping clinical algorithms with patient-specific information to identify patient's position on the treatment algorithm for guideline-based therapy recommendations.

METHODS

This study qualitatively assesses the therapy decision of clinical algorithms as part of a clinical pathway. The solution uses rule-based clinical algorithms, which were developed based on the corresponding guidelines. These algorithms are executed on a newly developed open-source workflow system and are visualized at the point of care. The aim of this approach is to create clinical algorithms based on an established business process standard, the Business Process Model and Notation (BPMN), which is annotated by UMLS terminologies. The gold standard for the validation process was set by manual extraction of clinical datasets from 86 rectal cancer patients and 89 colon cancer patients.

RESULTS

Using this approach, the algorithm achieved a precision value of 87.64% for colon cancer and 84.70% for rectal cancer with recall values of 87.64 and 83.72%, respectively.

CONCLUSION

The results indicate that the automatic positioning of a patient on the decision pathway is possible with tumor stages that have a less complex clinical algorithm with fewer decision points reaching a higher accuracy than complex stages.

Evaluating treatment modalities in chronic pain treatment by the multi-criteria decision making procedure

BACKGROUND

Chronic pain is one of the most common complaints of cancer patients. There are many pharmacological and non-pharmacological treatment modalities used for the treatment of pain. Nonetheless, non-pharmacological interventions are preferred because of potential side effects in cases resistant to medical therapy that require a dose increase or potent drug use. In most real-life situations, the decision on which technique to choose is based on the clinical but subjective decisions of the practitioners. This study aimed to find out the best non-pharmacological treatment option for patients with chronic cancer pain by following a rational and reasonable approach.

METHODS

Since the evaluation of treatment options requires to make a comparison between a number of alternatives in the light of certain criteria, we utilize the order relation analysis (G1-method) which is a method for determining the weights based on the improved Analytic Hierarchy Process (AHP). The method uses the relative importances on prioritizing the four criteria and eight sub-criteria defined by the experts of three pain physicians, one oncologist, and one oncologic surgeon. Four alternatives are then compared according to the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) using the verbal subjective judgments of the practitioners.

RESULTS

Obtained results indicate that the general medical condition of the patient and the stage of the cancer are the essential factors in the selection of the treatment method. It is followed by the extent of the pain and the level of evidence, respectively. According to the evaluations performed, spinal port and splanchnic nerve radiofrequency thermocoagulation treatments are the first and second priority methods for pain treatment, respectively, compared to lumbar epidural catheter and celiac plexus block.

CONCLUSIONS

The results of this study emphasize the need to integrate critical criteria into the decision-making process objectively. This is the first study in which multi-criteria decision-making tools are used in the evaluation and selection of pain management methods in cancer patients.

Health technology assessment (HTA) of optoelectronic biosensors for oncology by analytic hierarchy process (AHP) and Likert scale

BACKGROUND

The multicriteria decision method (MCDM) aims to find conflicts among alternatives by comparing and evaluating them according to various criteria to reach the best compromise solution. The evaluation of a new health technology is extremely important in the health sciences field. The aim of this work is to evaluate a new health technology to assay thyroglobulin in patients with differentiated thyroid cancer to improve its service from an organizational point of view, by planning new and appropriate training activities, ensuring proper use of resources and satisfying the needs of different users.

METHODS

The evaluation was performed using two methodologies: the analytic hierarchy process (AHP) and the Likert scale. The AHP is a multicriteria decision approach that assigns a weight to each evaluation criterion according to the decision maker's pairwise comparisons of the criteria. The Likert scale is a psychometric scale employed to study the degree of user satisfaction by measuring opinions.

RESULTS

Results show the need of particularly improving clinical efficiency, effectiveness, and return on sales (ROS) related to the technology; technological safety, human resources and other parameters do not need to be improved because of the high satisfaction results of the users.

CONCLUSIONS

The application of both methods provided the necessary information to improve the quality of the service, allowing the decision maker to identify the most valuable service features and to improve these to ensure user satisfaction and to identify possible service improvements.

Implementation of a scalable, web-based, automated clinical decision support risk-prediction tool for chronic kidney disease using C-CDA and application programming interfaces

Background and Objective

Clinical decision support tools for risk prediction are readily available, but typically require workflow interruptions and manual data entry so are rarely used. Due to new data interoperability standards for electronic health records (EHRs), other options are available. As a clinical case study, we sought to build a scalable, web-based system that would automate calculation of kidney failure risk and display clinical decision support to users in primary care practices.

Materials and Methods

We developed a single-page application, web server, database, and application programming interface to calculate and display kidney failure risk. Data were extracted from the EHR using the Consolidated Clinical Document Architecture interoperability standard for Continuity of Care Documents (CCDs). EHR users were presented with a noninterruptive alert on the patient's summary screen and a hyperlink to details and recommendations provided through a web application. Clinic schedules and CCDs were retrieved using existing application programming interfaces to the EHR, and we provided a clinical decision support hyperlink to the EHR as a service.

Results

We debugged a series of terminology and technical issues. The application was validated with data from 255 patients and subsequently deployed to 10 primary care clinics where, over the course of 1 year, 569 533 CCD documents were processed.

Conclusions

We validated the use of interoperable documents and open-source components to develop a low-cost tool for automated clinical decision support. Since Consolidated Clinical Document Architecture-based data extraction extends to any certified EHR, this demonstrates a successful modular approach to clinical decision support.

Evaluation of infectious diseases and clinical microbiology specialists' preferences for hand hygiene: analysis using the multi-attribute utility theory and the analytic hierarchy process methods

BACKGROUND

Hand hygiene is one of the most effective attempts to control nosocomial infections, and it is an important measure to avoid the transmission of pathogens. However, the compliance of healthcare workers (HCWs) with hand washing is still poor worldwide. Herein, we aimed to determine the best hand hygiene preference of the infectious diseases and clinical microbiology (IDCM) specialists to prevent transmission of microorganisms from one patient to another.

METHODS

Expert opinions regarding the criteria that influence the best hand hygiene preference were collected through a questionnaire via face-to-face interviews. Afterwards, these opinions were examined with two widely used multi-criteria decision analysis (MCDA) methods, the Multi-Attribute Utility Theory (MAUT) and the Analytic Hierarchy Process (AHP).

RESULTS

A total of 15 IDCM specialist opinions were collected from diverse private and public hospitals located in İzmir, Turkey. The mean age of the participants was 49.73 ± 8.46, and the mean experience year of the participants in their fields was 17.67 ± 11.98. The findings that we obtained through two distinct decision making methods, the MAUT and the AHP, suggest that alcohol-based antiseptic solution (ABAS) has the highest utility (0.86) and priority (0.69) among the experts' choices.

CONCLUSION

In conclusion, the MAUT and the AHP, decision models developed here indicate that rubbing the hands with ABAS is the most favorable choice for IDCM specialists to prevent nosocomial infection.

Predicting the incidence of portosplenomesenteric vein thrombosis in patients with acute pancreatitis using classification and regression tree algorithm

BACKGROUND AND OBJECTIVE

The accurate prediction of portosplenomesenteric vein thrombosis (PVT) in patients with acute pancreatitis(AP) is very important but may also be difficult because of our insufficient understanding of the characteristics of AP-induced PVT. The purpose of this study is to design a decision tree model that provides critical factors associated with PVT using an approach that makes use of classification and regression tree (CART) algorithm.

METHODS

The analysis included 353 patients with AP who were admitted between January 2011 and December 2015. CART model and logistic regression model were each applied to the same 50% of the sample to develop the predictive training models, and these models were tested on the remaining 50%. Statistical indexes were used to evaluate the value of the prediction in the 2 models.

RESULTS

The predicted sensitivity, specificity, positive predictive value, negative predictive value, and accuracy by CART for PVT were 78.0%, 87.2%, 64.0%, 93.2%, and 85.2%, respectively. Significant differences could be found between the CART model and the logistic regression model in these parameters. There were significant differences between the CART and logistic regression models in these parameters (P<.05). When the CART model was used to identify PVT, the area under receiver operating characteristic curve was 0.803, which demonstrated better overall properties than the logistic regression model (area under the curve=0.696) (95% confidence interval, 0.603-0.812).

CONCLUSION

The CART model based on serum amylase, d-dimer, Acute Physiology and Chronic Health Evaluation II, and prothrombin time is more likely to predict the occurrence of PVT induced by AP.

sandflyDST: a dynamic web-based decision support tool for the morphological identification of sandflies present in Anatolia and mainland Europe, and user study

Species identification of sandflies is mainly performed according to morphological characters using classical written identification keys. This study introduces a new web-based decision support tool (sandflyDST) for guiding the morphological identification of sandfly species present in Anatolia and mainland Europe and classified in the Phlebotomus and Sergentomyia genera (both: Diptera: Psychodidae). The current version of the tool consists of 111 questions and 36 drawings obtained from classical written keys, and 107 photographs for the quick and easy identification of 26 species of the genus Phlebotomus and four species of the genus Sergentomyia. The tool guides users through a decision tree using yes/no questions about the morphological characters of the specimen. The tool was applied by 30 individuals, who then completed study questionnaires. The results of subsequent analyses indicated that the usability (x‾SUSScore=75.4) and users' level of appreciation (86.6%) of the tool were quite high; almost all of the participants considered recommending the tool to others. The tool may also be useful in training new entomologists and maintaining their level of expertise. This is a dynamic tool and can be improved or upgraded according to feedback. The tool is now available online at http://parasitology.ege.edu.tr/sandflyDST/index.php.