Knowledge discovery in clinical decision support systems for pain management: A systematic review

ObjectiveThe occurrence of pain accounts for billions of dollars in annual medical expenditures; loss of quality of life and decreased worker productivity contribute to indirect costs. As pain is highly subjective, clinical decision support systems (CDSSs) can be critical for improving the accuracy of pain assessment and offering better support for clinical decision-making. This review is focused on computer technologies for pain management that allow CDSSs to obtain knowledge from the clinical data produced by either patients or health care professionals.Methods and materialsA comprehensive literature search was conducted in several electronic databases to identify relevant articles focused on computerised systems that constituted CDSSs and include data or results related to pain symptoms from patients with acute or chronic pain, published between 1992 and 2011 in the English language. In total, thirty-nine studies were analysed; thirty-two were selected from 1245 citations, and seven were obtained from reference tracking.ResultsThe results highlighted the following clusters of computer technologies: rule-based algorithms, artificial neural networks, nonstandard set theory, and statistical learning algorithms. In addition, several methodologies were found for content processing such as terminologies, questionnaires, and scores. The median accuracy ranged from 53% to 87.5%.ConclusionsComputer technologies that have been applied in CDSSs are important but not determinant in improving the systems’ accuracy and the clinical practice, as evidenced by the moderate correlation among the studies. However, these systems play an important role in the design of computerised systems oriented to a patient's symptoms as is required for pain management. Several limitations related to CDSSs were observed: the lack of integration with mobile devices, the reduced use of web-based interfaces, and scarce capabilities for data to be inserted by patients.     

Economic and organizational impact of a clinical decision support system on laboratory test ordering

Background

We studied the impact of a clinical decision support system (CDSS) implemented in a few wards of two Italian health care organizations on the ordering of redundant laboratory tests under different perspectives: (1) analysis of the volume of tests, (2) cost analysis, (3) end-user satisfaction before and after the installation of the CDSS.

Methods

(1) and (2) were performed by comparing the ordering of laboratory tests between an intervention group of wards where a CDSS was in use and a second (control) group where a CDSS was not in use; data were compared during a 3-month period before (2014) and a 3-month period after (2015) CDSS installation. To measure end-user satisfaction (3), a questionnaire based on POESUS was administered to the medical staff.

Results

After the introduction of the CDSS, the number of laboratory tests requested decreased by 16.44% and costs decreased by 16.53% in the intervention group, versus an increase in the number of tests (+3.75%) and of costs (+1.78%) in the control group. Feedback from practice showed that the medical staff was generally satisfied with the CDSS and perceived its benefits, but they were less satisfied with its technical performance in terms of slow response time.

Conclusions

The implementation of CDSSs can have a positive impact on both the efficiency of care provision and health care costs. The experience of using a CDSS can also result in good practice to be implemented by other health care organizations, considering the positive result from the first attempt to gather the point of view of end-users in Italy.

     

Change-point detection method for clinical decision support system rule monitoring

A clinical decision support system (CDSS) helps clinicians to manage patients, but malfunctions of its components or other systems on which it depends may affect its intended functions. Monitoring the system and detecting changes in its behavior that may indicate the malfunction can help to avoid any potential costs associated with its improper operation. In this paper, we investigate the problem of detecting changes in the CDSS operation, in particular its monitoring and alerting subsystem, by monitoring its rule firing counts. We aim to screen and detect changes in real-time, that is whenever a new datum (rule firing count) arrives, we want to have a score indicating how likely there is a change in the system. We develop a new method based on Seasonal-Trend decomposition with locally weighted regression (Loess) and likelihood ratio statistics to detect the changes. Experiments on daily rule-firing-count data collected from a real CDSS and known change-points show that our method improves the detection performance when compared with existing change-point detection methods.     

A patient-oriented clinical decision support system for CRC risk assessment and preventative care

Background

Colorectal Cancer (CRC) is the third leading cause of cancer death among men and women in the United States. Research has shown that the risk of CRC associates with genetic and lifestyle factors. It is possible to prevent or minimize certain CRC risks by adopting a healthy lifestyle. Existing Clinical Decision Support Systems (CDSS) mainly targeted physicians as the CDSS users. As a result, the availability of patient-oriented CDSS is limited. Our project is to develop patient-oriented CDSS for active CRC management.

Methods

We implemented an online patient-oriented CRC CDSS for the public to learn about CRC, assess CRC risk levels, understand personalized CRC risk factors, and seek professional advices for people with CRC concerns. The system is implemented based on the Django Model-View-Controller (MVC) framework with an extensible background MySQL database. A CRC absolute risk prediction model is applied to calculate the personalized CRC risk score with a user-friendly web survey. An interactive dashboard using advanced data visualization technics will display and interpret the risk scores and factors. Based on the risk assessment, a structured decision tree algorithm will provide the recommendations on customized CRC screening methods. The CDSS also provides a search function for preferred providers and hospitals based on geographical information and patient preferences.

Results

A prototype of the patient-oriented CRC CDSS has been developed. It provides an open assessment of potential CRC risks via an online survey. The CRC risk predictive model has been implemented. The prediction outcomes of risk levels and factors are presented to the users through a personalized interactive visualization interface, to guide the public on how to reduce the CRC risks by changing their living styles (such as smoking and drinking) and diet characteristics (such as consumptions of red meat and milk). The CDSS will also provide customized recommendations on screening methods based on the corresponding risk factors. For users seeking professional clinicians, the CDSS also provides a convenient tool for searching nearby hospitals and available doctors based on the location preferences and providers characteristics (such as gender, language, and specialty).

Conclusions

This CRC CDSS prototype provides a patient-friendly interface for CRC risk assessment and gives a personalized interpretation on important CRC risk factors. It is a useful tool to educate the public on CRC, to provide guidance on minimizing CRC risks, and to promote early CRC screening that reduces the CRC occurrences.

     

Integrating computerized clinical decision support systems into clinical work: A meta-synthesis of qualitative research

PurposeComputerized clinical decision support systems (CDSS) are an emerging means for improving healthcare safety, quality and efficiency, but meta-analyses findings are mixed. This meta-synthesis aggregates qualitative research findings as possible explanations for variable quantitative research outcomes.Inclusion criteriaQualitative studies published between 2000 and 2013 in English, involving physicians, registered and advanced practice nurses’ experience of CDSS use in clinical practice were included.Search strategyPubMed and CINAHL databases were searched. Study titles and abstracts were screened against inclusion criteria. Retained studies were appraised against quality criteria. Findings were extracted iteratively from studies in the 4th quartile of quality scores. Two reviewers constructed themes inductively. A third reviewer applied the defined themes deductively achieving 92% agreement.Results3798 unique records were returned; 56 met inclusion criteria and were reviewed against quality criteria. 9 studies were of sufficiently high quality for synthetic analysis. Five major themes (clinician-patient-system integration; user interface usability; the need for better ‘algorithms’; system maturity; patient safety) were defined.ConclusionsDespite ongoing development, CDSS remains an emerging technology. Lack of understanding about and lack of consideration for the interaction between human decision makers and CDSS is a major reason for poor system adoption and use. Further high-quality qualitative research is needed to better understand human—system interaction issues. These issues may continue to confound quantitative study results if not addressed.     

Necessity of clinical decision support system created by medical staff

To support patient safety, we have established a new system that collates medical facility clinical records, examination results and orders, and implementation information comprehensively in real time, checks for consistency and validity, and sends warnings to the appropriate people at the appropriate time. Because our system actually corrects inaccurate operation information, it is different from most existing facilities for patient safety in that it reconstructs information independently from the HIS (Hospital Information System). We were permitted to send warning messages not only to the doctor who entered the orders, but also to the chief of medical staff and team members. For the warning method, we tried screen flashes and chimes, mobile phone messages, and high quality interactive voice responses. We also investigated the degree of message usefulness. Therein, by not relying on "authenticity" and "readability," but by exhaustively collecting and appropriately revising in alignment with the use of information, we have created an original system that collects accurate information. This original system was established by medical staff members. The appropriate revisions mentioned herein are items which meticulously reflect the medical professional's comments and selected operation and signify why a "Clinical Decision Support System created by medical staff" is necessary.     

A decision support system for cost-effective diagnosis

ObjectiveSpeed, cost, and accuracy are three important goals in disease diagnosis. This paper proposes a machine learning-based expert system algorithm to optimize these goals and assist diagnostic decisions in a sequential decision-making setting.MethodsThe algorithm consists of three components that work together to identify the sequence of diagnostic tests that attains the treatment or no test threshold probability for a query case with adequate certainty: lazy-learning classifiers, confident diagnosis, and locally sequential feature selection (LSFS). Speed-based and cost-based objective functions can be used as criteria to select tests.ResultsResults of four different datasets are consistent. All LSFS functions significantly reduce tests and costs. Average cost savings for heart disease, thyroid disease, diabetes, and hepatitis datasets are 50%, 57%, 22%, and 34%, respectively. Average test savings are 55%, 73%, 24%, and 39%, respectively. Accuracies are similar to or better than the baseline (the classifier that uses all available tests in the dataset).ConclusionWe have demonstrated a new approach that dynamically estimates and determines the optimal sequence of tests that provides the most information (or disease probability) based on a patient's available information.     

A computerized data management system for a regional clinical virology laboratory

A computerized virology data management system has been developed. MUMPS language with a conversational mode program was used to allow technical and clerical staff to operate the system with minimal training time investment. It can be readily applied to any microbiology system.     

Good laboratory management and clinical laboratory physician

Medical expenses have been increasing annually, and reducing expenses while maintaining effective medical care is desirable. In the late 1990s, Japanese government introduced policies expected to improve the medical security system. In the clinical laboratory field, some revisions such as packaging of certain tests(blanket test), separation between performance and interpretation fees for laboratory test, proper use of tumor markers, and additional fees for sample management. Japanese government also wants the clinical laboratory to return accurate laboratory test result to patients and physicians. Laboratory physicians have to make a great effort to manage clinical laboratories according to the guideline for GIOs of laboratory physicians from the Japanese Society of Clinical Pathology. The laboratory physician is the key person for good laboratory management.     

CAP quality management system in clinical laboratory and its issue

The CAP (College of American Pathologists) was established in 1962 and, at present, CAP-accredited laboratories include about 6000 institutions all over the world, mainly in the U.S. The essential purpose of CAP accreditation is high quality reservation and improvement of clinical laboratory services for patient care, and is based on seven points, listed below. (1) Establishment of a laboratory management program and laboratory techniques to assure accuracy and improve overall quality of laboratory services. (2) Maintenance and improvement of accuracy objectively by centering on a CAP survey. (3) Thoroughness in safety and health administration. (4) Reservation of the performance of laboratory services by personnel and proficiency management. (5) Provision of appropriate information to physicians, and contribution to improved quality of patient care by close communication with physicians (improvement in patient care). (6) Reduction of running costs and personnel costs based on evidence by employing the above-mentioned criteria. (7) Reduction of laboratory error. In the future, accreditation and/or certification by organizations such as CAP, ISO, etc., may become a requirement for providing any clinical laboratory services in Japan. Taking the essence of the CAP and the characteristics of the new international standard, ISO151589, into consideration, it is important to choose the best suited accreditation and/or certification depending of the purpose of clinical laboratory.     

The future of clinical laboratory database management system]

To assess the present status of the clinical laboratory database management system, the difference between the Clinical Laboratory Information System and Clinical Laboratory System was explained in this study. Although three kinds of database management systems (DBMS) were shown including the relational model, tree model and network model, the relational model was found to be the best DBMS for the clinical laboratory database based on our experience and developments of some clinical laboratory expert systems. As a future clinical laboratory database management system, the IC card system connected to an automatic chemical analyzer was proposed for personal health data management and a microscope/video system was proposed for dynamic data management of leukocytes or bacteria.     

Grand challenges in clinical decision support

There is a pressing need for high-quality, effective means of designing, developing, presenting, implementing, evaluating, and maintaining all types of clinical decision support capabilities for clinicians, patients and consumers. Using an iterative, consensus-building process we identified a rank-ordered list of the top 10 grand challenges in clinical decision support. This list was created to educate and inspire researchers, developers, funders, and policy-makers. The list of challenges in order of importance that they be solved if patients and organizations are to begin realizing the fullest benefits possible of these systems consists of: improve the human-computer interface; disseminate best practices in CDS design, development, and implementation; summarize patient-level information; prioritize and filter recommendations to the user; create an architecture for sharing executable CDS modules and services; combine recommendations for patients with co-morbidities; prioritize CDS content development and implementation; create internet-accessible clinical decision support repositories; use freetext information to drive clinical decision support; mine large clinical databases to create new CDS. Identification of solutions to these challenges is critical if clinical decision support is to achieve its potential and improve the quality, safety and efficiency of healthcare.     

A framework and model for evaluating clinical decision support architectures

In this paper, we develop a four-phase model for evaluating architectures for clinical decision support that focuses on: defining a set of desirable features for a decision support architecture; building a proof-of-concept prototype; demonstrating that the architecture is useful by showing that it can be integrated with existing decision support systems and comparing its coverage to that of other architectures. We apply this framework to several well-known decision support architectures, including Arden Syntax, GLIF, SEBASTIAN, and SAGE.     

Nursing process decision support system for urology ward

PurposeWe developed a nursing process decision support system (NPDSS) based on three clinical pathways, including benign prostatic hypertrophy, inguinal hernia, and urinary tract stone. NPDSS included six major nursing diagnoses – acute pain, impaired urinary elimination, impaired skin integrity, anxiety, infection risk, and risk of falling. This paper aims to describe the design, development and validation process of the NPDSS.MethodsWe deployed the Delphi method to reach consensus for decision support rules of NPDSS. A team of nine-member expert nurses from a medical center in Taiwan was involved in Delphi method. The Cronbach's α method was used for examining the reliability of the questionnaire used in the Delphi method. The Visual Basic 6.0 as front-end and Microsoft Access 2003 as back-end was used to develop the system. A team of six nursing experts was asked to evaluate the usability of the developed systems. A 5-point Likert scale questionnaire was used for the evaluation. The sensitivity and specificity of NPDSS were validated using 150 nursing chart.ResultsThe study showed a consistency between the diagnoses of the developed system (NPDSS) and the nursing charts. The sensitivities of the nursing diagnoses including acute pain, impaired urinary elimination, risk of infection, and risk of falling were 96.9%, 98.1%, 94.9%, and 89.9% respectively; and the specificities were 88%, 49.5%, 62%, and 88% respectively. We did not calculate the sensitivity and specificity of impaired skin integrity and anxiety due to non-availability of enough sample size.ConclusionsNPDSS can help nurses in decision making of nursing diagnoses. Besides, it can help them to generate nursing diagnoses based on patient-specific data, individualized care plans, and implementation within their usual nursing workflow.     

Recommended practices for computerized clinical decision support and knowledge management in community settings: a qualitative study

Background

The purpose of this study was to identify recommended practices for computerized clinical decision support (CDS) development and implementation and for knowledge management (KM) processes in ambulatory clinics and community hospitals using commercial or locally developed systems in the U.S.

Methods

Guided by the Multiple Perspectives Framework, the authors conducted ethnographic field studies at two community hospitals and five ambulatory clinic organizations across the U.S. Using a Rapid Assessment Process, a multidisciplinary research team: gathered preliminary assessment data; conducted on-site interviews, observations, and field surveys; analyzed data using both template and grounded methods; and developed universal themes. A panel of experts produced recommended practices.

Results

The team identified ten themes related to CDS and KM. These include: 1) workflow; 2) knowledge management; 3) data as a foundation for CDS; 4) user computer interaction; 5) measurement and metrics; 6) governance; 7) translation for collaboration; 8) the meaning of CDS; 9) roles of special, essential people; and 10) communication, training, and support. Experts developed recommendations about each theme. The original Multiple Perspectives framework was modified to make explicit a new theoretical construct, that of Translational Interaction.

Conclusions

These ten themes represent areas that need attention if a clinic or community hospital plans to implement and successfully utilize CDS. In addition, they have implications for workforce education, research, and national-level policy development. The Translational Interaction construct could guide future applied informatics research endeavors.     

基于大数据技术的糖尿病临床决策支持系统分析与展望

糖尿病是一种慢性非传染性疾病,目前只能通过长期用药和自我管理来缓解病情,无法根治.临床决策支持系统能够模拟糖尿病医疗专家诊断疾病的思维过程,向医生提供常规诊疗方案,推荐最优方案.现有的临床决策支持系统大多基于临床指南、规则、案例推理以及本体.大数据技术可获取和处理多元异构的各类数据,提供更科学的个性化诊疗方案.近年来已有基于决策树、神经网络、模糊逻辑、支持向量机、APRIORI关联规则与多维分析和时序挖掘等多种大数据处理方法应用于糖尿病的临床诊断,但其尚处于起步阶段.对基于大数据技术的糖尿病临床决策支持系统的框架进行了分析,并展望了未来的诊疗方式.