Der Analytic Hierarchy Process (AHP): Eine Methode zur Entscheidungsunterstützung im Gesundheitswesen

Comparative Analysis of Three Predictive Models of Performance Indicators with Results-Based Management: Cancer Data Statistics in a National Institute of Health

Predictive models play a crucial role in RBMs to analyze performance indicator results to manage unexpected events and make timely decisions to resolve them. Their use in Mexico is deficient, and monitoring and evaluation are among the weakest pillars of the model. In response to these needs, the aim of this study was to perform a comparative analysis of three predictive models to analyze 10 medical performance indicators and cancer data related to children with cancer. To accomplish these purposes, a comparative and retrospective study with nonprobabilistic convenience sampling was conducted. The predictive models were exponential smoothing, autoregressive integrated moving average, and linear regression. The lowest mean absolute error was used to identify the best model. Linear regression performed best regarding nine of the ten indicators, with seven showing p < 0.05. Three of their assumptions were checked using the Shapiro-Wilk, Cook's distance, and Breusch-Pagan tests. Predictive models with RBM are a valid and relevant instrument for monitoring and evaluating performance indicator results to support forecasting and decision-making based on evidence and must be promoted for use with cancer data statistics. The place numbers obtained by cancer disease inside the main causes of death, morbidity and hospital outpatients in a National Institute of Health were presented as evidence of the importance of implementing performance indicators associated with children with cancer.

Psychological, Structural, Social and Economic Determinants of Suicide Attempt: Risk Assessment and Decision Making Strategies

The aim of this research was to conduct a risk assessment and management of psychological, structural, social and economic determinants (PSSED) in a suicide attempt. The sample consisted of 353 individuals who had a recorded history of suicidal attempt; and 20 professional individuals by purposive sampling method within a descriptive cross-sectional design. Worksheets for RAM and AHP were used for data collection in this study. The rate of suicide attempt was 7.21 per 100,000 population in this study. Analysis showed that depression and mental disorders; personality disorders; family problems; socio-cultural and economic problems; lack of awareness; and low level of education have a high level of risk for suicide attempts. Psychiatric and psychological services; awareness and knowledge of life skills; medical services to dysfunctional families; development of community-based planning for PSSED of suicide; and employment and entrepreneurship services may lower suicide attempt risk.

Do They Align? Congruence Between Patient Preferences of People Living with Cognitive Impairments and Physicians' Judgements for Person-Centered Care: An Analytic Hierarchy Process Study

BACKGROUND

Person-centered care (PCC) requires knowledge about patient preferences. Among people living with cognitive impairments (PlwCI), evidence on quantitative, choice-based preferences, which allow to quantify, weigh, and rank care elements, is limited. Furthermore, data on the congruence of patient preferences with physicians' judgements for PCC are missing. Such information is expected to support the implementation of PCC; state-of-the-art medical care aligned with patients' preferences.

OBJECTIVE

To elicit patient preferences and physicians' judgements for PCC and their congruence.

METHODS

Data from the mixed-methods PreDemCare study, including a cross-sectional, paper-and-pencil, interviewer-assisted analytic hierarchy process (AHP) survey conducted with n = 50 community-dwelling PlwCI and n = 25 physicians. Individual AHP weights (preferences/judgements) were calculated with the principal eigenvector method and aggregated per group by aggregation of individual priorities mode. Individual consistency ratios (CRs) were calculated and aggregated per group. Group differences in preferences/judgements were investigated descriptively by means and standard deviations (SDs) of AHP weights, resulting ranks, and boxplots. Additionally, differences between groups were investigated with independent paired t-test/Mann Whitney U-test. Sensitivity of AHP results was tested by inclusion/exclusion of inconsistent respondents, with an accepted threshold at CR≤0.3 for patients, and CR≤0.2 for physicians, due to better cognitive fitness of the latter group.

RESULTS

Patient preferences and physicians' judgements did not differ significantly, except for the criterion Memory Exercises (AHP weights (mean (SD)): 0.135 (0.066) versus 0.099 (0.068), p = 0.01). We did not see rank-reversals of criteria after exclusion of inconsistent participants. Mean CR for patients at the criteria level was 0.261, and 0.181 for physicians.

CONCLUSION

Physicians' judgements in our setting aligned well with patients' preferences. Our findings may be used to guide the implementation of preference-based PCC.

Development of a Quantitative Preference Instrument for Person-Centered Dementia Care-Stage 2: Insights from a Formative Qualitative Study to Design and Pretest a Dementia-Friendly Analytic Hierarchy Process Survey

Person-centered care (PCC) requires knowledge about patient preferences. An analytic hierarchy process (AHP) is one approach to quantify, weigh and rank patient preferences suitable for People living with Dementia (PlwD), due to simple pairwise comparisons of individual criteria from a complex decision problem. The objective of the present study was to design and pretest a dementia-friendly AHP survey. Methods: Two expert panels consisting of n = 4 Dementia Care Managers and n = 4 physicians to ensure content-validity, and "thinking-aloud" interviews with n = 11 PlwD and n = 3 family caregivers to ensure the face validity of the AHP survey. Following a semi-structured interview guide, PlwD were asked to assess appropriateness and comprehensibility. Data, field notes and partial interview transcripts were analyzed with a constant comparative approach, and feedback was incorporated continuously until PlwD had no further comments or struggles with survey completion. Consistency ratios (CRs) were calculated with Microsoft® Excel and ExpertChoice Comparion®. Results: Three main categories with sub-categories emerged: (1) Content: clear task introduction, (sub)criteria description, criteria homogeneity, (sub)criteria appropriateness, retest questions and sociodemography for heterogeneity; (2) Format: survey structure, pairwise comparison sequence, survey length, graphical design (incl. AHP scale), survey procedure explanation, survey assistance and response perspective; and (3) Layout: easy wording, short sentences and visual aids. Individual CRs ranged from 0.08 to 0.859, and the consolidated CR was 0.37 (0.038). Conclusions: Our formative qualitative study provides initial data for the design of a dementia-friendly AHP survey. Consideration of our findings may contribute to face and content validity in future quantitative preference research in dementia.

Elicitation of quantitative, choice-based preferences for Person-Centered Care among People living with Dementia in comparison to physicians' judgements in Germany: study protocol for the mixed-methods PreDemCare-study

BACKGROUND

Person-Centered-Care (PCC) requires knowledge about patient preferences. Among People-living-with-Dementia (PlwD) data on quantitative, choice-based preferences, which would allow to quantify, weigh and rank patient-relevant elements of dementia-care, and identify most/least preferred choices, are limited. The Analytic-Hierarchy-Process (AHP) may be one approach to elicit quantitative, choice-based preferences with PlwD, due to simple pairwise comparisons of individual criteria from a complex decision-problem, e.g. health care decisions. Furthermore, data on congruence of patient preferences with physicians' judgements for PCC are missing. If patient preferences and physicians' judgements differ, provision of PCC becomes unlikely. An understanding of patient preferences compared to physician's judgements will support the implementation of truly PCC, i.e. state of the art dementia-care aligned with patient preferences.

METHODS

This mixed-methods-study will be based on the results from a previous systematic review and conducted in three phases: (I) literature-based key intervention-categories of PCC will be investigated during qualitative interviews with Dementia-Care-Managers (DCMs) and PlwD to identify actually patient-relevant (sub) criteria of PCC; (II) based on findings from phase I, an AHP-survey will be designed and pre-tested for face- and content-validity, and consistency during face-to-face "thinking-aloud"-interviews with PlwD and two expert panels (DCMs and physicians); (III) the developed survey will elicit patient preferences and physicians' judgements for PCC. To assess individual importance weights for (sub) criteria in both groups, the Principal-Eigenvector-Method will be applied. Weights will be aggregated per group by Aggregation-of-Individual-Priorities-mode. Descriptive and interferential statistical analyses will be conducted to assess congruence of importance-weights between groups. Subgroup-analyses shall investigate participant-heterogeneities, sensitivity of AHP-results shall be tested by inclusion/exclusion of inconsistent respondents.

DISCUSSION

Little research is published on quantitative, choice-based preferences in dementia care. We expect that (1) PlwD have preferences and can express these, (2) that the AHP is a suitable technique to elicit quantitative, choice-based preferences among PlwD, and (3) to identify a divergence between patient preferences and physicians' judgements for PCC. With the help of the AHP-technique, which supports systematic decision-making including multiple criteria, it may be possible to involve PlwD in future care decisions (patient participation) and ensure implementation of truly Person-Centered-Dementia-Care.

TRIAL REGISTRATION

Approval of the study was granted by the Ethics Committee at the University Medicine Greifswald the 09Apr2021 (Reg.-Nr.: BB 018-21, BB 018-21a, BB 018-21b).

Development of a Quantitative Instrument to Elicit Patient Preferences for Person-Centered Dementia Care Stage 1: A Formative Qualitative Study to Identify Patient Relevant Criteria for Experimental Design of an Analytic Hierarchy Process

Background: Person-centered care (PCC) requires knowledge about patient preferences. This formative qualitative study aimed to identify (sub)criteria of PCC for the design of a quantitative, choice-based instrument to elicit patient preferences for person-centered dementia care. Method: Interviews were conducted with n = 2 dementia care managers, n = 10 People living with Dementia (PlwD), and n = 3 caregivers (CGs), which followed a semi-structured interview guide including a card game with PCC criteria identified from the literature. Criteria cards were shown to explore the PlwD's conception. PlwD were asked to rank the cards to identify patient-relevant criteria of PCC. Audios were verbatim-transcribed and analyzed with qualitative content analysis. Card game results were coded on a 10-point-scale, and sums and means for criteria were calculated. Results: Six criteria with two sub-criteria emerged from the analysis; social relationships (indirect contact, direct contact), cognitive training (passive, active), organization of care (decentralized structures and no shared decision making, centralized structures and shared decision making), assistance with daily activities (professional, family member), characteristics of care professionals (empathy, education and work experience) and physical activities (alone, group). Dementia-sensitive wording and balance between comprehensibility vs. completeness of the (sub)criteria emerged as additional themes. Conclusions: Our formative study provides initial data about patient-relevant criteria of PCC to design a quantitative patient preference instrument. Future research may want to consider the balance between (sub)criteria comprehensibility vs. completeness.

A Method for Evaluating and Selecting Suitable Hardware for Deployment of Embedded System on UAVs

The use of UAVs for remote sensing is increasing. In this paper, we demonstrate a method for evaluating and selecting suitable hardware to be used for deployment of algorithms for UAV-based remote sensing under considerations of Size, Weight, Power, and Computational constraints. These constraints hinder the deployment of rapidly evolving computer vision and robotics algorithms on UAVs, because they require intricate knowledge about the system and architecture to allow for effective implementation. We propose integrating computational monitoring techniques—profiling—with an industry standard specifying software quality—ISO 25000—and fusing both in a decision-making model—the analytic hierarchy process—to provide an informed decision basis for deploying embedded systems in the context of UAV-based remote sensing. One software package is combined in three software–hardware alternatives, which are profiled in hardware-in-the-loop simulations. Three objectives are used as inputs for the decision-making process. A Monte Carlo simulation provides insights into which decision-making parameters lead to which preferred alternative. Results indicate that local weights significantly influence the preference of an alternative. The approach enables relating complex parameters, leading to informed decisions about which hardware is deemed suitable for deployment in which case.

Shaping an Effective Health Information Website on Rare Diseases Using a Group Decision-Making Tool: Inclusion of the Perspectives of Patients, Their Family Members, and Physicians

BACKGROUND

Despite diverging definitions on rare conditions, people suffering from rare diseases share similar difficulties. A lack of experience by health professionals, a long wait from first symptoms to diagnosis, scarce medical and scientific knowledge, and unsatisfactory treatment options all trigger the search for health information by patients, family members, and physicians. Examining and systematically integrating stakeholder needs can help design information platforms that effectively support this search.

OBJECTIVE

The aim of this study was to innovate on the group decision-making process involving patients, family members, and physicians for the establishment of a national rare disease Internet platform. We determined differences in the relevance of health information-especially examining quantifiable preference weights-between these subgroups and elucidated the structure and distribution of these differences in people suffering from rare diseases, their family members, and physicians, thus providing information crucial to their collaboration.

METHODS

The included items were identified using a systematic Internet research and verified through a qualitative interview study. The identified major information needs included medical issues, research, social help offers, and current events. These categories further comprised sublevels of diagnosis, therapy, general disease pattern, current studies, study results, registers, psychosocial counseling, self-help, and sociolegal advice. The analytic hierarchy process was selected as the group decision-making tool. A sensitivity analysis was used to determine the stability and distribution of results. t tests were utilized to examine the results' significance.

RESULTS

A total of 176 questionnaires were collected; we excluded some questionnaires in line with our chosen consistency level of 0.2. Ultimately, 120 patients, 24 family members, and 32 physicians participated in the study (48 men and 128 women, mean age=48 years, age range=17-87 years). Rankings and preference weights were highly heterogeneous. Global ranking positions of patients, family members, and physicians are shown in parentheses, as follows: medical issues (3/4, 4, 4), research (3/4, 2/3, 3), social help offers (1, 2/3, 2), and current events (2, 1, 1); diagnosis (6, 8, 9), therapy (5, 9, 7), general disease pattern (9, 4/5/6, 6), current studies (7, 4/5/6, 3), study results (8, 7, 8), registers (4, 1, 5), psychosocial counseling (1, 2, 4), self-help (3, 3, 2), and sociolegal advice (2, 4/5/6, 1). Differences were verified for patients for 5 information categories (P=.03), physicians for 6 information categories (P=.03), and family members for 4 information categories (P=.04).

CONCLUSIONS

Our results offer a clear-cut information structure that can transparently translate group decisions into practice. Furthermore, we found different preference structures for rare disease information among patients, family members, and physicians. Some websites already address differences in comprehension between those subgroups. Similar to pharmaceutical companies, health information providers on rare diseases should also acknowledge different information needs to improve the accessibility of information.

Measuring patients' priorities using the Analytic Hierarchy Process in comparison with Best-Worst-Scaling and rating cards: methodological aspects and ranking tasks

BACKGROUND

Identifying patient priorities and preference measurements have gained importance as patients claim a more active role in health care decision making. Due to the variety of existing methods, it is challenging to define an appropriate method for each decision problem. This study demonstrates the impact of the non-standardized Analytic Hierarchy Process (AHP) method on priorities, and compares it with Best-Worst-Scaling (BWS) and ranking card methods.

METHODS

We investigated AHP results for different Consistency Ratio (CR) thresholds, aggregation methods, and sensitivity analyses. We also compared criteria rankings of AHP with BWS and ranking cards results by Kendall's tau b.

RESULTS

The sample for our decision analysis consisted of 39 patients with rare diseases and mean age of 53.82 years. The mean weights of the two groups of CR ≤ 0.1 and CR ≤ 0.2 did not differ significantly. For the aggregation by individual priority (AIP) method, the CR was higher than for aggregation by individual judgment (AIJ). In contrast, the weights of AIJ were similar compared to AIP, but some criteria's rankings differed. Weights aggregated by geometric mean, median, and mean showed deviating results and rank reversals. Sensitivity analyses showed instable rankings. Moderate to high correlations between the rankings resulting from AHP and BWS.

LIMITATIONS

Limitations were the small sample size and the heterogeneity of the patients with different rare diseases.

CONCLUSION

In the AHP method, the number of included patients is associated with the threshold of the CR and choice of the aggregation method, whereas both directions of influence could be demonstrated. Therefore, it is important to implement standards for the AHP method. The choice of method should depend on the trade-off between the burden for participants and possibilities for analyses.

Making Good Decisions in Healthcare with Multi-Criteria Decision Analysis: The Use, Current Research and Future Development of MCDA

Healthcare decision making is usually characterized by a low degree of transparency. The demand for transparent decision processes can be fulfilled only when assessment, appraisal and decisions about health technologies are performed under a systematic construct of benefit assessment. The benefit of an intervention is often multidimensional and, thus, must be represented by several decision criteria. Complex decision problems require an assessment and appraisal of various criteria; therefore, a decision process that systematically identifies the best available alternative and enables an optimal and transparent decision is needed. For that reason, decision criteria must be weighted and goal achievement must be scored for all alternatives. Methods of multi-criteria decision analysis (MCDA) are available to analyse and appraise multiple clinical endpoints and structure complex decision problems in healthcare decision making. By means of MCDA, value judgments, priorities and preferences of patients, insurees and experts can be integrated systematically and transparently into the decision-making process. This article describes the MCDA framework and identifies potential areas where MCDA can be of use (e.g. approval, guidelines and reimbursement/pricing of health technologies). A literature search was performed to identify current research in healthcare. The results showed that healthcare decision making is addressing the problem of multiple decision criteria and is focusing on the future development and use of techniques to weight and score different decision criteria. This article emphasizes the use and future benefit of MCDA.

Applying the Analytic Hierarchy Process in healthcare research: A systematic literature review and evaluation of reporting

Background

The Analytic Hierarchy Process (AHP), developed by Saaty in the late 1970s, is one of the methods for multi-criteria decision making. The AHP disaggregates a complex decision problem into different hierarchical levels. The weight for each criterion and alternative are judged in pairwise comparisons and priorities are calculated by the Eigenvector method. The slowly increasing application of the AHP was the motivation for this study to explore the current state of its methodology in the healthcare context.

Methods

A systematic literature review was conducted by searching the Pubmed and Web of Science databases for articles with the following keywords in their titles or abstracts: “Analytic Hierarchy Process,” “Analytical Hierarchy Process,” “multi-criteria decision analysis,” “multiple criteria decision,” “stated preference,” and “pairwise comparison.” In addition, we developed reporting criteria to indicate whether the authors reported important aspects and evaluated the resulting studies’ reporting.

Results

The systematic review resulted in 121 articles. The number of studies applying AHP has increased since 2005. Most studies were from Asia (almost 30 %), followed by the US (25.6 %). On average, the studies used 19.64 criteria throughout their hierarchical levels. Furthermore, we restricted a detailed analysis to those articles published within the last 5 years (n = 69). The mean of participants in these studies were 109, whereas we identified major differences in how the surveys were conducted. The evaluation of reporting showed that the mean of reported elements was about 6.75 out of 10. Thus, 12 out of 69 studies reported less than half of the criteria.

Conclusion

The AHP has been applied inconsistently in healthcare research. A minority of studies described all the relevant aspects. Thus, the statements in this review may be biased, as they are restricted to the information available in the papers. Hence, further research is required to discover who should be interviewed and how, how inconsistent answers should be dealt with, and how the outcome and stability of the results should be presented. In addition, we need new insights to determine which target group can best handle the challenges of the AHP.