scholarly journals Polypharmacy Management in the Older Adults: A Scoping Review of Available Interventions

2021 ◽  
Vol 12 ◽  
Author(s):  
M. Kurczewska-Michalak ◽  
P. Lewek ◽  
B. Jankowska-Polańska ◽  
A. Giardini ◽  
N. Granata ◽  
...  
Keyword(s):  
Older Adults ◽  
Scoping Review ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Healthcare Services ◽  
Practical Reasons ◽  
Potentially Inappropriate Medications ◽  
Medication Appropriateness Index ◽  
Effective Strategies ◽  
Explicit Criteria

Background: Polypharmacy paves the way for non-adherence, adverse drug reactions, negative health outcomes, increased use of healthcare services and rising costs. Since it is most prevalent in the older adults, there is an urgent need for introducing effective strategies to prevent and manage the problem in this age group.Purpose: To perform a scoping review critically analysing the available literature referring to the issue of polypharmacy management in the older adults and provide narrative summary.Data sources: Articles published between January 2010–March 2018 indexed in CINHAL, EMBASE and PubMed addressing polypharmacy management in the older adults.Results: Our search identified 49 papers. Among the identified interventions, the most often recommended ones involved various types of drug reviews based on either implicit or explicit criteria. Implicit criteria-based approaches are used infrequently due to their subjectivity, and limited implementability. Most of the publications advocate the use of explicit criteria, such as e.g. STOPP/START, Beers and Medication Appropriateness Index (MAI). However, their applicability is also limited due to long lists of potentially inappropriate medications covered. To overcome this obstacle, such instruments are often embedded in computerised clinical decision support systems.Conclusion: Multiple approaches towards polypharmacy management are advised in current literature. They vary in terms of their complexity, applicability and usability, and no “gold standard” is identifiable. For practical reasons, explicit criteria-based drug reviews seem to be advisable. Having in mind that in general, polypharmacy management in the older adults is underused, both individual stakeholders, as well as policymakers should strengthen their efforts to promote these activities more strongly.

scholarly journals Diagnosis of Rare Diseases: a scoping review of clinical decision support systems

2020 ◽  
Vol 15 (1) ◽  
Author(s):  
Jannik Schaaf ◽  
Martin Sedlmayr ◽  
Johanna Schaefer ◽  
Holger Storf
Keyword(s):  
Decision Support ◽  
Data Integration ◽  
Decision Support Systems ◽  
Clinical Decision Support ◽  
Clinical Data ◽  
Rare Diseases ◽  
Scoping Review ◽  
Support Systems ◽  
Clinical Decision Support Systems ◽  
Clinical Decision

Abstract Background Rare Diseases (RDs), which are defined as diseases affecting no more than 5 out of 10,000 people, are often severe, chronic and life-threatening. A main problem is the delay in diagnosing RDs. Clinical decision support systems (CDSSs) for RDs are software systems to support clinicians in the diagnosis of patients with RDs. Due to their clinical importance, we conducted a scoping review to determine which CDSSs are available to support the diagnosis of RDs patients, whether the CDSSs are available to be used by clinicians and which functionalities and data are used to provide decision support. Methods We searched PubMed for CDSSs in RDs published between December 16, 2008 and December 16, 2018. Only English articles, original peer reviewed journals and conference papers describing a clinical prototype or a routine use of CDSSs were included. For data charting, we used the data items “Objective and background of the publication/project”, “System or project name”, “Functionality”, “Type of clinical data”, “Rare Diseases covered”, “Development status”, “System availability”, “Data entry and integration”, “Last software update” and “Clinical usage”. Results The search identified 636 articles. After title and abstracting screening, as well as assessing the eligibility criteria for full-text screening, 22 articles describing 19 different CDSSs were identified. Three types of CDSSs were classified: “Analysis or comparison of genetic and phenotypic data,” “machine learning” and “information retrieval”. Twelve of nineteen CDSSs use phenotypic and genetic data, followed by clinical data, literature databases and patient questionnaires. Fourteen of nineteen CDSSs are fully developed systems and therefore publicly available. Data can be entered or uploaded manually in six CDSSs, whereas for four CDSSs no information for data integration was available. Only seven CDSSs allow further ways of data integration. thirteen CDSS do not provide information about clinical usage. Conclusions Different CDSS for various purposes are available, yet clinicians have to determine which is best for their patient. To allow a more precise usage, future research has to focus on CDSSs RDs data integration, clinical usage and updating clinical knowledge. It remains interesting which of the CDSSs will be used and maintained in the future.

scholarly journals A scoping review of knowledge authoring tools used for developing computerized clinical decision support systems

JAMIA Open ◽  
2021 ◽  
Vol 4 (4) ◽  
Author(s):  
Sujith Surendran Nair ◽  
Chenyu Li ◽  
Ritu Doijad ◽  
Paul Nagy ◽  
Harold Lehmann ◽  
...  
Keyword(s):  
Decision Support ◽  
Clinical Decision Support ◽  
Scoping Review ◽  
System Integration ◽  
Clinical Decision Support Systems ◽  
Application Programming Interface ◽  
Clinical Decision ◽  
Authoring Tools ◽  
Clinical Models ◽  
Digital Knowledge

Abstract Objective Clinical Knowledge Authoring Tools (CKATs) are integral to the computerized Clinical Decision Support (CDS) development life cycle. CKATs enable authors to generate accurate, complete, and reliable digital knowledge artifacts in a relatively efficient and affordable manner. This scoping review aims to compare knowledge authoring tools and derive the common features of CKATs. Materials and Methods We performed a keyword-based literature search, followed by a snowball search, to identify peer-reviewed publications describing the development or use of CKATs. We used PubMed and Embase search engines to perform the initial search (n = 1579). After removing duplicate articles, nonrelevant manuscripts, and not peer-reviewed publication, we identified 47 eligible studies describing 33 unique CKATs. The reviewed CKATs were further assessed, and salient characteristics were extracted and grouped as common CKAT features. Results Among the identified CKATs, 55% use an open source platform, 70% provide an application programming interface for CDS system integration, and 79% provide features to validate/test the knowledge. The majority of the reviewed CKATs describe the flow of information, offer a graphical user interface for knowledge authors, and provide intellisense coding features (94%, 97%, and 97%, respectively). The composed list of criteria for CKAT included topics such as simulating the clinical setting, validating the knowledge, standardized clinical models and vocabulary, and domain independence. None of the reviewed CKATs met all common criteria. Conclusion Our scoping review highlights the key specifications for a CKAT. The CKAT specification proposed in this review can guide CDS authors in developing more targeted CKATs.

scholarly journals A scoping review of clinical decision support tools that generate new knowledge to support decision making in real time

2020 ◽  
Vol 27 (12) ◽  
pp. 1968-1976
Author(s):  
Anna Ostropolets ◽  
Linying Zhang ◽  
George Hripcsak
Keyword(s):  
Decision Support ◽  
Real Time ◽  
Clinical Decision Support ◽  
Scoping Review ◽  
Clinical Care ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
The United States ◽  
Quality Outcomes ◽  
New Knowledge

Abstract Objective A growing body of observational data enabled its secondary use to facilitate clinical care for complex cases not covered by the existing evidence. We conducted a scoping review to characterize clinical decision support systems (CDSSs) that generate new knowledge to provide guidance for such cases in real time. Materials and Methods PubMed, Embase, ProQuest, and IEEE Xplore were searched up to May 2020. The abstracts were screened by 2 reviewers. Full texts of the relevant articles were reviewed by the first author and approved by the second reviewer, accompanied by the screening of articles’ references. The details of design, implementation and evaluation of included CDSSs were extracted. Results Our search returned 3427 articles, 53 of which describing 25 CDSSs were selected. We identified 8 expert-based and 17 data-driven tools. Sixteen (64%) tools were developed in the United States, with the others mostly in Europe. Most of the tools (n = 16, 64%) were implemented in 1 site, with only 5 being actively used in clinical practice. Patient or quality outcomes were assessed for 3 (18%) CDSSs, 4 (16%) underwent user acceptance or usage testing and 7 (28%) functional testing. Conclusions We found a number of CDSSs that generate new knowledge, although only 1 addressed confounding and bias. Overall, the tools lacked demonstration of their utility. Improvement in clinical and quality outcomes were shown only for a few CDSSs, while the benefits of the others remain unclear. This review suggests a need for a further testing of such CDSSs and, if appropriate, their dissemination.

scholarly journals Title Assessing Potentially Inappropriate Medications in Seniors: Differences between American Geriatrics Society and STOPP Criteria, and Preventing Adverse Drug Reactions

Geriatrics ◽  
2020 ◽  
Vol 5 (4) ◽  
pp. 68
Author(s):  
Roger E. Thomas ◽  
Leonard T. Nguyen
Keyword(s):  
At Risk ◽  
Adverse Drug Reactions ◽  
Pearson Correlation ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Discharge Summary ◽  
Potentially Inappropriate Medications ◽  
Drug Reactions ◽  
Patients At Risk ◽  
Inappropriate Medications

Key problems for seniors are their exposure to “potentially inappropriate medications” and “potential medication omissions”, which place them at risk for moderate, severe, or fatal adverse drug reactions. This study of 82,935 first admissions to acute care hospitals in Calgary during 2013–2018 identified 294,160 Screening Tool of Older People’s Prescriptions (STOPP) potentially inappropriate medications (PIMs) (3.55/patient), 226,970 American Geriatric Society (AGS) Beers PIMs (2.74/patient), 59,396 START potential prescribing omissions (PPOs) (0.72/patient), and 85,288 STOPP PPOs (1.03/patient) for which a new prescription corrected the omission. This represents an overwhelming workload to prevent inappropriate prescriptions continuing during the hospitalisation and then deprescribe them judiciously. Limiting scrutiny to the most frequent PIMs and PPOs will identify many moderate, severe, or fatal risks of causing adverse drug reactions (ADRs) but to identify all PIMs or PPO involving moderate or severe risks of ADRs also involves searching lower in the frequency list of patients. Deciding whether to use the STOPP or AGS Beers PIM lists is an important issue in searching for ADRs, because the Pearson correlation coefficient for agreement between the STOPP and AGS Beers PIM totals in this study was 0.7051 (95% CI 0.7016 to 0.7085; p < 0.001). The combined lists include 289 individual PIM medications but STOPP and AGS have only 159 (55%) in common. The AGS Beers lists include medications used in the US and STOPP/START those used in Europe. The AGS authors recommend using both criteria. The ideal solution to the problem is to implement carefully constructed Clinical Decision Support Systems (CDSS) as in the SENATOR trial, then for an experienced pharmacist to focus on the key PIMs and PPOs likely to lead to moderate, severe, or fatal ADRs. The pharmacist and key decision makers on the services need to establish a collegial relationship to discuss frequently changing the medications that place the patients at risk. Then, the remaining PIMs and PPOs that relate to chronic disease management can be discussed by phone with the family physician using the discharge summary, which lists the medications for potential deprescribing.

scholarly journals Clinical Decision Support Systems for Diagnosis in Primary Care: A Scoping Review

2021 ◽  
Vol 18 (16) ◽  
pp. 8435
Author(s):  
Taku Harada ◽  
Taiju Miyagami ◽  
Kotaro Kunitomo ◽  
Taro Shimizu
Keyword(s):  
Primary Care ◽  
Decision Support ◽  
Decision Support Systems ◽  
Chronic Diseases ◽  
Clinical Decision Support ◽  
Scoping Review ◽  
Support Systems ◽  
Diagnostic Errors ◽  
Clinical Decision Support Systems ◽  
Clinical Decision

Diagnosis is one of the crucial tasks performed by primary care physicians; however, primary care is at high risk of diagnostic errors due to the characteristics and uncertainties associated with the field. Prevention of diagnostic errors in primary care requires urgent action, and one of the possible methods is the use of health information technology. Its modes such as clinical decision support systems (CDSS) have been demonstrated to improve the quality of care in a variety of medical settings, including hospitals and primary care centers, though its usefulness in the diagnostic domain is still unknown. We conducted a scoping review to confirm the usefulness of the CDSS in the diagnostic domain in primary care and to identify areas that need to be explored. Search terms were chosen to cover the three dimensions of interest: decision support systems, diagnosis, and primary care. A total of 26 studies were included in the review. As a result, we found that the CDSS and reminder tools have significant effects on screening for common chronic diseases; however, the CDSS has not yet been fully validated for the diagnosis of acute and uncommon chronic diseases. Moreover, there were few studies involving non-physicians.

Clinical Decision Support Systems

2005 ◽  
pp. 251-270 ◽  
Author(s):  
Spyretta Golemati ◽  
Stavroula Mougiakakou ◽  
John Stoitsis ◽  
Ioannis Valavanis ◽  
Konstantina S. Nikita
Keyword(s):  
Decision Support ◽  
Clinical Decision Support ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Quality Of Healthcare ◽  
Healthcare Services ◽  
Patient Data ◽  
Patient Treatment ◽  
Biomedical Knowledge ◽  
Time Required

This chapter introduces the basic principles of Clinical Decision Support (CDS) systems. CDS systems aim to codify and strategically manage biomedical knowledge to handle challenges in clinical practice using mathematical modelling tools, medical data processing techniques and Artificial Intelligence (AI) methods. CDS systems cover a wide range of applications, from diagnosis support to modelling the possibility of occurrence of various diseases or the efficiency of alternative therapeutic schemes, using not only individual patient data but also data on risk factors and efficiency of available therapeutic schemes stored in databases. Computer-Aided Diagnosis (CAD) systems can enhance the diagnostic capabilities of physicians and reduce the time required for accurate diagnosis. Modern Therapeutic Decision Support (TDS) systemsmake use of advanced modelling techniques and available patient data to optimise and individualise patient treatment. CDS systems aim to improve the overall health of the population by improving the quality of healthcare services, as well as by controlling the cost-effectiveness of medical examinations and treatment.

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