Year : 2017 | Volume
: 10 | Issue : 2 | Page : 321--333
The assessment of patient clinical outcome: A literature discussion
Mou’ath A Hourani, Nidal M Turab, Qusai Y Shambour
Faculty of Information Technology, Al-Ahliyya Amman University, Amman, Jordan
Qusai Y Shambour
Al-Ahliyya Amman University, Amman
A patient's safety in clinical field is critical, important and complex. The patients are still suffering from preventable harms from diagnostic errors, procedure mistakes, teamwork failures, and the failure to deliver recommended therapies. Patient outcome is the status upon a patient's adherence to treatment. An assessment of patient's clinical outcome is one of the important aspects of patient safety, and requires the assessment of the benefits, harms and risks of therapeutic options and comparing between them. Very few methods are developed for the clinical field and there is still a need for more accurate methods for such assessment. To achieve the above objective, we have performed an integrative review of the literature using different online databases and search engines including PubMed, Scopus, Google, and Google Scholar to explore current issues regarding the assessment of patient clinical outcome. This paper presents:
Based on the literature research in this paper, researchers, clinicians and health care professionals working in the field of assessment of patient clinical outcome, will be able to
- an overview of the existing assessment methods for patient clinical outcome and their conceptual limitations; and
- a discussion of the primitiveness of the current assessment methods.
- understand all the critical issues in this area, and
- design and develop novel general methods for the assessment of patient clinical outcome that avoid the conceptual limitations of existing methods.
|How to cite this article:|
Hourani MA, Turab NM, Shambour QY. The assessment of patient clinical outcome: A literature discussion.Ann Trop Med Public Health 2017;10:321-333
|How to cite this URL:|
Hourani MA, Turab NM, Shambour QY. The assessment of patient clinical outcome: A literature discussion. Ann Trop Med Public Health [serial online] 2017 [cited 2020 Feb 19 ];10:321-333
Available from: http://www.atmph.org/text.asp?2017/10/2/321/208728
Clinical Outcome Assessments (COAs) can be used to determine whether or not a drug has been demonstrated to provide desired treatment benefit. COA measures include: Patient-reported outcome (PRO) measures; Clinician-reported outcome (ClinRO) measures; and Observer-reported outcome (ObsRO) measures.
Patient-reported outcome (PRO) is a questioner or method for collecting responses from the patient directly or by interviewers. A well-designed PRO questionnaire should consider single and multiple characteristics. These characteristics are known as constructs while the questionnaire that used to collect these characteristics is known as instrument or tool. Typically, PRO tools must be validated and tested extensively., If questionnaires were designed to collect the characteristics of any disease population and cover a wide range of aspects it is known as generic; while it is known as condition-targeted if it designed to measure the characteristics of people with particular medical situations. Unidimensional questionnaire measures a single characteristic; it has a scale of single score. A multi-dimensional questionnaire measures multiple characteristics and provides multiple scales; there is a separate report of each scale.
Recently, there have been proposed quick, effective and understandable tools to observe patient clinical outcomes on a regular basis. These tools allow patients to record clinical outcomes and experience in a semi-structured way and accordingly synoptic input data, while their physio-emotional sensitivity tracked automatically. Modern advancements in psychometrics such as Item Response Theory (IRT) and Computerized Adaptive Testing (CAT) are used to create reliable and validated measurement tools as part of the National Institute of Health's Roadmap Initiative.
The need for an assessment of patient clinical outcome is compulsory, clinical outcome assessment methods in some clinical fields have been employed without sufficient understanding of their characteristics. This insufficient understanding of the outcome assessment methods characteristics trigger the need for more research to better understand the existing patient clinical outcome assessment methods and their conceptual limitations. Consequently, researchers, clinicians and health care professionals will be able to design and develop novel general methods for the assessment of patient clinical outcome. Such methods will consider the conceptual merits of existing methods and avoid their conceptual limitations.
Accordingly, this paper is proposing a literature on the assessment of patient clinical outcome that will help researchers, clinicians and health care professionals to design novel general methods for the assessment of patient clinical outcome that avoid the conceptual limitations of existing methods. The literature is organized as follows: [Section 2] describe the literature search methodology used in this paper. [Section 3] presents an overview of the existing methods for patient outcome assessment and their conceptual limitations. A discussion of the primitiveness of the current assessment methods is shown at [Section 4]. Finally conclusion is given at [Section 5].
Method of Literature Search
The information reported in this review was obtained from different online databases and search engines including PubMed, Scopus, Google, and Google Scholar. Keywords and expressions used for the search included Patient, Clinical Outcome, Healthcare, Assessment Method, Assessment of Clinical Outcome, Benefit-risk analysis, Quality-of-Life, Treatments, Cost-Effectiveness Analysis, and similar pre-identified terms were used in separate searches and in conjunction with each other to identify all related publications. Related publications published in English including Journal papers, books and reports were scrutinized to identify those that met a criteria of presenting information accredited to the purpose of this review. After elimination of studies that were not relevant to the subject matter, a total of 180 articles were reviewed and cited in this study.
Overview of the existing methods for patient clinical outcome assessment and their conceptual limitations
There are a number of methods to assess the benefits, harms, risk and patient outcome from different views; a summary of those methods are:
Categorizing of the Severity of Adverse Event and Disease States
In this approach, the severity of disease state is classified into specific degrees. One of the tries in this approach is the categorizing of severity of disease state into seven degrees ranging from mild disease or condition with symptoms, which are not progressive and which only cause a mild degree of discomfort or incapacity to life-threatening condition. Severity degrees of adverse events and diseases were assigned by group of physicians, converted to numerical scale of severity, and combined with data on frequency of benefits and adverse events to make quantitative assessment of drug benefits and harms. Another try was repeated for the assessment of severity categories of adverse events by collecting family doctors opinions using five point category scales instead of seven.
Recently, again, a six-category scale of severity for adverse events was used. Every degree of severity is assigned a score ranged from zero to one. The severity scores were then combined with the scores derived from an ADR causality algorithm by taking the average of both scores.
Conceptual Limitations of Categorizing of the Severity of Adverse Event and Disease States
Adverse event severity grading do not have international acceptance. It is intuitive, subjective assessment of safety, and based upon personal opinions. Scoring available evidence is not definitive and grades definitions are not satisfactory. Grades are set according to general rather than solid estimation without paying attention to different characteristics of the adverse effect. They are biased and don't provide robust, consistent and valid clinical decision making. Moreover, those methods don't define elements of benefit and risks and don't allow for tradeoffs between multiple elements.
Evidence-based benefit and risk model
In Evidence-based Benefit and Risk Model, the benefit is estimated by efficacy, responder rate, and data evidence; while risk is estimated by adverse drug reaction seriousness, adverse drug reaction frequency, and data evidence.
Conceptual limitations of evidence-based benefit and risk model
Evidence-based Benefit and Risk Model lacks a conceptual framework for trading-off the benefits and harms. The criteria are not comprehensive and not well defined, and cannot be expressed in equivalent units. In addition, patient health preferences are not considered.
Principle of Threes
This method is calculating the benefit score as the product of disease cure rate times disease seriousness times disease duration, and adverse event score as the product of adverse event incidence times adverse event seriousness times adverse event duration; each parameter in benefit and risk is rated as (low=1, medium=2, high=3).
Transparent uniform risk benefit overview (TURBO)
The TURBO model is a quantitative and graphical method for benefit risk analysis; the risk factor R is calculated as the sum of two risks: the risk associated with the most serious adverse effect (severity score from 1 to 5), and the risk associated with the second most serious adverse effect or the most frequent adverse effect (severity score from 1 to 2). The Benefit factor B is calculated as the sum of the primary benefit, which is the change(s) in health status and social capabilities (score from 1 to 5), and the ancillary benefit (score from 1 to 2). The R factor and B factor are then represented in a diagram.
Conceptual limitations of principle of threes and turbomodel
The categorization of disease seriousness in both models is subjective, and lacks a conceptual framework for trading-off the benefits and harms. Both models have limited number,, and not comprehensive benefit and risk criteria.,, Moreover, patient health preferences are not considered. Both models are too simplistic for even moderately complex cases.
Benefit-less-risk analysis (BLRA)
In this method, risk is represented by five different body functions selected to be of concern to the situation under consideration; next, each body functioning is assigned an intensity grade; then, the importance weight of each body functioning to the others are set by patient and reflect patient's overall well-being; after that, the intensity grades for different body functioning are combined using the importance weight of each body functioning. Benefit is typically measured on a smaller number of endpoints. Then, risk weighted score is multiplied by a conversion factor, the result is subtracted from the benefit score.,
Conceptual limitations of benefit-less-risk analysis (BLRA)
Benefit and risk in Benefit-Less-Risk Analysis (BLRA) method are not clearly defined,,, interpretation is very complex,, and requires extensive quantification., Also, setting of the conversion factor requires some critical thinking. The assigned weights are subjective and subject to bias, and may affect validity., Finally, it does not account for multiple adverse events. Seeking more transparent method is necessary.
In this method, benefits and harms are estimated by three categories of benefit and three categories of harm, creating a three by three table; None/minimal, major, and (near) remission for benefit, and none/minimal, major, and (near) death for harm.
Conceptual limitations of omeract 3*3
Categorizing and weighting of benefits and harms in this method are subjective. Three categories only simplifies the matters and does not replace deeper analyses. The patient health preferences are also not considered.
Number needed to treat (NNT)
NNT Concept is defined as the inverse of the Absolute Risk Reduction (ARR). The number needed to treat is the average number of patients needed to be treated to prevent an adverse outcome in one additional patient compared to a control treatment group;,,, in other words, number needed to treat is the number of people who need to be treated over a defined time to achieve the required outcome in one of them. Physicians are widely using the number needed to treat because it is usually reported as an integer and easy to understand; it's understanding is relatively straightforward.
Where P1 is the proportion of the disease of interest in the control group, and P2 the proportion of disease in the treatment group.
Absolute risk reduction (ARR)
ARR is the arithmetic difference between the incidence of harm condition of concern in the treatment group and the incidence of harm condition of concern in the control group.,,, It is suggested to use preferably more than NNT for both theoretical and practical reasons.
Conceptual limitations of number needed to treat and absolute risk reduction-based methods
NNT and ARR-based methods does not consider multiple benefits and harms,,,,, and does not account for utilities,,, and time dimension,, of outcomes. Those limitations are exceeded only by The Adjusted Number Needed To Treat method, and account for utilities only is considered by Relative-Value Adjusted Number-Needed-To-Treat method.
NNT and ARR-based methods compare only two therapeutic options at a time; the situation will be more complex when comparing more than two therapeutic options., NNT and ARR-based methods does not consider successful outcomes that are associated or not associated with treatment-related adverse events;, this limitation is exceeded by NNTUS and NNHUF methods.
NNT do not have good statistical properties;, among them is that when the denominator (ARR) is zero, in which the result of NNT and NNT-based methods become not interpretable and biased., Also, the sums of different NNTs, can give meaningless results. It is concluded that the nature of NNT scale is biased.,,
Another weakness of those methods is that the severity importance of the adverse event relative to the benefit is not considered.,,, Moreover, NNT and ARR-based methods are dependent on baseline risk; this will lead to limited generalizability. It is inappropriate to extrapolate the results of NNT and ARR-based methods from one population to another population with a different baseline risk, and results are only applicable in similar settings for both populations.,, Those methods also have significant limitations in terms of comprehensiveness and comparability, they are not suitable for making value judgments, and are not helpful for communicating harms. The combining of two rates in one statistical measure was poorly expressed.
Minimal necessary efficacy of the treatments
It is also named as Minimum Clinical Efficacy (MCE)., MCE calculates weighting of the benefit and the risk of a specific treatment. It aims to find the minimal therapeutic benefit at which a treatment is still worth administering. It includes the calculations of benefits and risks for the new, old and no treatments to achieve this goal. Risks of treatment of the disease and multiple adverse event profiles outcome are estimated by mortality and morbidity. The benefit is presented in terms of number needed to treat, relative risk reduction, and outcome utilities. Outcome utilities are expressed by length of life, absence of pain, cost, and the strength of individual patient preference for an outcome. Patient preference for an outcome is represented by the probability of patients who will be free from consequences of the disease or toxicity of the treatment. MCE considers the natural characteristics of the disease in the general population.
Conceptual limitations of minimal necessary efficacy of the treatments
Minimal Necessary Efficacy is difficult to explain to patients and stakeholders. It does not include uncertainty in the benefit or risk measurements. Values of the method need subjective and study-specific judgments. While it is based on NNT, it inherits NNT limitations.
Disability-adjusted life years (DALYs)
The disability-adjusted life years (DALYs) had been developed in order to calculate the loss associated with premature mortality and disability. Severity of disability is classified into six categories, ranging from class 1 which is “limited ability to perform at least one activity in one of the areas of recreation, education, procreation or occupation” to class 6 which is “needs assistance with activities of daily living such as eating, personal hygiene or toilet use”. Classification of disabilities into the six classes and severity weighting of each class is set by expert panel., The weights of class severity are between zero and one. The unit of measure for the burden of disease is time (in Daly); DALYs are calculated by the sum of years lost from premature mortality and the years lost from disability., For example, a woman with disability for ten years and disability weight of 0.4 and then she died ten years prematurely; her loss in health would be 14 DALYs; that is, the sum of the 10 years of lost life plus the four-year loss (10 x 0.4) from the disability. Cost-effectiveness then could be directly calculated for each therapeutic option.
Quality-adjusted life years (QALYs)
The QALYs method is measuring both the quality and the quantity of life lived., QALYs are the product of life expectancy (in years) and its quality (utility) over that time (estimated in QALY units); each year of life considered is given a coefficient between 0 and 1;,, 0 represents the value or utility score for death and 1 represents normal full health.,, Thus, ten years of life expectancy at a utility of 0.5 is equivalent to five years with full health. The patients estimate subjectively their own lived years-quality with handicap or serious discomfort by different methods like time trade off method, standard gamble, or from generic health-state questionnaires. Time trade off and standard gamble methods are discussed later in this chapter. Cost-effectiveness/efficacy then could be directly calculated for each therapeutic option.
Conceptual limitations of quality-adjusted life years (QALYs) and disability-adjusted life years (DALYs)
QALYs and DALYs are complex, abstract, and controversial, especially with the estimation of value preferences. Since preferences are totally subjective, results may open serious objections. There are some concerns about the validity, and the application of those methods for decision-making. The concerns about the sensitivity of such methods for measuring differences in health outcomes are fortified. QALYs life-years unit and utility unit are not the same, joining them directly compromises QALYs arithmetic concept; to obtain coherent results, both scales should be expressed in the same measurement unit. The same conclusion could be applied on DALYs.
The role of both methods in benefit-risk assessment remains unclear., The conceptual basis for the both metrics is flawed., They are weak in acknowledging uncertainty of the outcomes. They also discriminate against patients having limited treatment potential. An example is if two patients are suffering the same health condition, but one has another disabling-health condition, and the other does not have; QALYs method will discriminate and give the priority for the non-disabled patient for the treatment of the shared health condition because his/her quality of life will improve more than the quality of life of the disabled patient. This discrimination causes deep and unresolved difficulty for use of cost-effectiveness analysis with QALYs to prioritize health care.
QALYs method is inappropriate to use as a basis for the comparison of different health outcomes, and health technologies. QALYs don't perfectly measure the quality of life of various health conditions nor perfectly measure health states using interval scale. It ignores salient societal concerns regarding resource allocation, and health care priorities based on it cause public discomfort. QALYs bias against palliative treatments that do not shorten premature death or improve the quality of life of patients with long life expectancies. QALYs can be measured in different ways yielding different unreliable results, and should not be used for decision making. While QALYs is using utility health methods, QALYs inherits their limitations.
DALYs is rough measure, and not sensitive enough to capture the patient outcomes; they consider all disabilities regardless their type or severity to be equal, which restricts its ability to rank various interventions. The DALYs cannot replace traditional methods to assess disease, and treatment in clinical practice. The validity of DALYs depends largely on the validity of the DALYs panel's composition and the underlying assumptions. DALYs devalue the lives of women because they do not consider for social differences and how their lives are lived. Critics argue that lives of the patient with disabilities worth less than the patient without disabilities by counting a year lived with a disability as less than a full year., Because of that, it will drive away resources from disabled patients.,
DALYs' disability-concept does not accord with that in WHO's International Classification of Functioning, Disability, and Health (ICF). DALYs approach ignores equity or acts directly counter to it. It does not reflect life with a disability as experienced by disabled people. Also, it does not take in consideration needs that patients with different functional statuses might have. Moreover, it does not value interventions that enhance the lives of disabled patients.
Disability-free life expectancy
Disability-free life expectancy deducts the disability years from life expectancy regardless the severity of different disabilities, and no weighting is used to differentiate between them.
Conceptual limitations of disability-free life expectancy
It has DALYs' limitations. Moreover, it is a very rough estimate and do not consider the nature and severity of different disability conditions at all.
Time without symptoms of disease and toxic effects (TWIST), and quality adjusted time without symptoms of disease and toxic effects (Q-TWIST)
Time without symptoms of disease and toxicity of treatment (TWIST) is set to provide a single metric of length and quality of survival. Time with subjective adverse effects of treatment and time with unpleasant symptoms of disease are subtracted from overall survival time to calculate TWIST for each patient. Health state preferences are estimated by assigning subjective weights.
Quality Adjusted Time without Symptoms of Disease and Toxic Effects (Q-TWIST) uses a quality of life index (utility) to estimate the survival in which a day with low quality will not considered as a whole day, but a fraction of a day; the fraction is estimated by the utility weighting applied to that day.,
Conceptual limitations of time without symptoms of disease and toxic effects (TWIST), and quality adjusted time without symptoms of disease and toxic effects (Q-TWIST)
Both TWIST and Q-TWIST are intuitive. Their utility weightings also are subjective, challenging, and not explicit. There are some concerns about the validity, and the application of those methods for decision-making. For Q-TWIST, using different utility techniques yield different results, and while Q-TWIST is using utility health methods, Q-TWIST inherit their limitations.
Incremental net health benefit (INHB)
Net Health Benefit (NHB) is the difference between the sum of the weighted benefits and the sum of the weighted risks of a treatment. Benefits and adverse events of a treatment are quantified using available clinical trial or post-marketing surveillance data. Importance weights of each outcome are usually incorporated using QALYs., The difference between the NHB of a treatment and the NHB of an alternative treatment or standard of care represents the Incremental Net Health Benefit (INHB).,, A positive INHB means that the net benefits of the treatment are positive relative to the comparator.
Conceptual limitations of incremental net health benefit (INHB)
INHB is a decision aid not a replacement for expert judgment. It is difficult to explain to patients and stakeholders. While it is using QALYs method, INHB inherits its limitations.
The clinical utility index (CUI)
Clinical utility is defined as the net benefit of therapy as perceived by the physician or expertise. A CUI gives value for each attribute in the product profile using physician preference data, or by relying on expert opinion to provide a single metric for multiple dimensions of benefit and risk.,,, The CUI can be expressed mathematically by the following equation:
Where W is the weight of the attribute and U represents the utility function. The weight of the attribute represents the relative importance of each attribute to the others. Utility represents the clinically meaningful differences of an attribute. Each utility can be assigned value between zero (worst outcome) and one (best outcome) for both efficacy and safety measures. Another approach assumes that efficacy attributes are expressed with positive values (0 to 1) and safety attributes are expressed with negative values (-1 to 0). Because of the subjectivity in deciding the weights and clinically meaningful differences for each attribute, a sensitivity analysis is usually performed., The use of external data to minimize subjectivity, weighting and definition of clinical cut off are all important.
Multiple-criteria decision-making techniques
Multiple-criteria decision-making techniques include decision trees, value trees, and other techniques. Decision trees are usually used for solving problems of choice under uncertainty with multiple objectives as usually takes place in different clinical decisions. Decision trees aids modeling the logical flow of clinical problems and determines the best choice among multiple options by calculating probabilities of events and inserting the valuations of possible outcomes. They can handle the data of different types. Decision trees are a sequential probabilistic branches from an initial state of health or medical intervention that branches from left to right.
The first step of building a decision tree is to set a list for different therapy options with their consequences, every option is branched into one or more branches, every branch represents a consequence of the therapy option with probability of occurrence. Every sub branch could be also divided into more branches and so on until reaching the end outcomes in the most right level. Then, every end outcome is assigned a utility. After that, the utility of every end outcome is multiplied by the probabilities of all of its above branches, and the sum of all end outcomes scores is calculated for every therapeutic option. The therapy with the highest value represents the best option. Finally, sensitivity analysis is performed. Sensitivity analysis includes changing repeatedly the probabilities and weights of the criteria across a plausible range and testing the impact of this change on the overall decision.,,,
Value trees are almost sharing the same structure with decision trees. It handles problems of choice with multiple objectives under uncertainty. An example of implementing the value trees in the clinical field is the Multi Criteria Decision Analysis (MCDA) approach, which is used to evaluate the benefit risk ratio of medicines., In this approach, complex problems are partitioned into more manageable parts, which can be studied using data and judgment. The partitions are then recombined after scoring and weighting using computer software to provide a consistent overall picture for the decision makers. The approach helps thinking for decision-makers to be more explicit, consistent and transparent in their discussions, but does not replace their judgment or take decisions.,
The first step in this approach is to detect a list of benefit and risk criteria, which form benefit-risk profile, and detection of the options to be evaluated. Then, for every option, all criteria are modeled graphically using value tree. After that, MCDA sub classifies every benefit and risk criterion in the value tree. Then, every criterion for an option in the most right level of the tree is scored using a scale and every criterion is assigned a weight to reflect its relative importance to the others. Scoring weights are set by experts. After that, the product of every criterion score for an option time's criterion's weight is calculated, and the sum of the products is calculated for both benefits and risks. Then, the total scores of benefits and risks are compared for every option.,,,, The final output will be a single risk-adjusted benefit resulted from collapsed multiple dimensions. The last step is running sensitivity analysis to detect the importance of each criterion and its impact on the result.,,,
Examples of the benefit criteria in this model are efficacy versus comparator and its clinical relevance, statistical adequacy of the trial, statistical significance of the efficacy results. Examples of the Risk criteria are overall incidence of adverse effects, Overall incidence of serious adverse effects, discontinuation rate due to adverse effects, incidence, seriousness, duration and reversibility of specific adverse effects, safety in subgroups, drugs and food interactions with other.
Another example which is similar to the MCDA technique is The Benefit-Risk Assessment Model (BRAM). BRAM includes evaluative judgments with relevant data to provide a platform for trading off multiple benefit and risk components in a transparent and consistent manner. In this model, a branched hierarchy of benefits and risks are presented instead of the value tree. Benefit here includes efficacy, life effects, and convenience, and risk includes safety, tolerability, and improper use of drugs.
Other example of using value tree is the BRAT Framework, which is developed by The Benefit Risk Action Team, and it is a set of processes and tools for selecting, organizing, summarizing, communicating, and interpreting data for benefit-risk assessments. It provides a standardized and flexible platform for incorporating outcomes and preference weights for decision-making.,
This method first defines the decision context, which includes drug, dose, formulation, indication, patient population, comparator(s), and time horizon for outcomes. Second, it identifies all-important outcomes and creates the initial value tree. Then, it assesses outcome importance by applying any ranking or weighting of outcome importance to decision makers or other stakeholders., The BRAT framework does not apply any particular method for weighting, and does not require the use of weights. The value tree could be updated as new data or more precise definitions of the outcome end points or measures become available.
Examples of benefits are reducing pain, reduction in functional disability and other specific case-related benefits. Example of risk is different individual case-related risks.,
Previous models have some merits. They combine judgments numerically in a transparent way.,, The balance of benefits and risks can be evaluated for multi-therapy and against placebo, or against active control. The models consider a comprehensive benefit and risk criteria of potential relevance, and one or more additional benefit and risk criteria could be added in flexible way.,, They enable a discussion and trade-offs about the subjective weights., They also consider potential uncertainty in the case of incompleteness of the evidence. They demonstrate the value of the social effect, and they are applicable to all kinds of medicines and medical devices because they can handle data of mixed type.
Conceptual limitations of multiple-criteria decision-making techniques
Multiple-Criteria Decision-Making Techniques can't generate decisions; instead, they serve as a aid to thinking and decision-making. Setting up the model is time consuming, burdensome, and require building up a complex model for every situation, therapeutic area or even product or indication.,,,, There are no constant benefit and risk safety criteria, and utilities with their limitations are usually used for this evaluation. The results of the models are uncertain, and a danger may take place if a decision over relies on them. Additionally, optimal weights of the criteria are not guaranteed because weights are subjective, and require tradeoff between models' criteria. Decision makers also may not achieve a consensus about the weights. These models do not support the calculation of relative benefit, harm and risk of therapeutic options in a health system; instead, they estimate the benefits and risks for every clinical case alone., The models are complex to explain to patients and nontechnical stakeholders. Models' results are snapshots in time. Finally, some of those models do not apply any weights, which is a limitation by itself.
Discussion of the primitiveness of the current methods
The process of assessment of benefits, harms, risks, and patient clinical outcome is still primitive, wanting and rudimentary, primarily undefined, or ill-defined, not well-developed,, biased and limited to make informed decisions, in its infancy and early stage,,,, and is largely uncharted. It relies on subjective judgment of experts, and rarely done in a quantitative fashion. A consistent quantitative assessment structure is lacking, and there is very little or no well-established and recognized approach on how to do it.,,, It is not typically performed, not presented in a consistent, systematic, analytical and integrated framework using single scale,,, and not standardized., Currently, there is no general universal solution available for the assessment process,, and this process is still subject to continuous development.,
The estimation of the ratio between the benefits of drugs and their harms is not obvious or easy and not applicable in a straightforward manner.,, Although benefits and harms assessment is the core of the drug development, standardized and validated quantitative conceptual models, which measures patient outcome are lacking., This also leads to lack of consistency in the comparison of pharmaceuticals, and health programs.
Using the term benefit-risk ratio without explaining its meaning is common in the literature. There is no single, clear definition of “benefits” and “risks”. Rarely, any quantitative analysis is used or attempted to synthesize in the articles where benefit, and risk words are mentioned in the title, and even in the highest impact medical journals, benefits and harms evidence is not consistently presented to make direct interpretation much easier. Some researchers are not willing to adopt a quantitative benefit-risk assessment because it does not accurately represent the whole picture to patient.
It is frustrating, that there is no generally agreed metric or methodology, and no standard with widely acknowledged definitions for benefit-risk assessment.,, Benefits and harms are not usefully combined into one scale. There are no standards in which comparisons against might be made nor clearly showing the benefits or harms of treatments in a clinically useful way. The common practice of providing separate lists of benefits and adverse effects cannot be justified as a scientific analysis and the decision made relying on them will be subjective., Also, there is no method for measuring the quality of the decision made. This process is necessary to track how safety is being monitored and assessed.
The current US system for assessing the risks of therapeutics is outdated and inadequate. Regulatory authorities in EU, US and Japan did not issue criteria for benefit and risk assessment. Neither the US Food and Drug Administration (FDA) nor the CHMP have released methods for benefit-risk analyses. The available methods of analyses are limited to non-regulatory situations, and research domain. The public's health techniques for the detection, verification, and quantification of safety issues are also scattered and disappointing but could be improved.
Benefits, harms, and risk assessment is more than the subjective opinion of a group of experts. The benefit-risk assessment differs between countries, and regulatory authorities differ in the threshold for taking action and for handling of therapeutic risk management plans. The definition of benefit can be quite varied. Decisions are made on a relatively informal and irrational basis. Because of the subjective judgments, evaluation process is varied between different assessors and assessments. Different regulatory authorities and countries have different decisions and actions using the same data inconsistently.,,
There is an increasing importance of cost-effectiveness analysis., Assessment of therapy effectiveness is primary driver of cost-effectiveness analysis and economic modeling. Any economic evaluation should be based on a representation of the effectiveness data. There is a lack of consensus on evaluation criteria and standards for cost-effectiveness analysis and economic modelling, and how to weigh those criteria. Economic models can have political, discriminatory, or arbitrary biases and there are many shortcomings in the existing cost-effectiveness models, which consequently affect the legitimacy of their recommendations. In addition, they fail to identify existing misallocation of resources. The procedures to assure transparency for many of these models are also unclear. Unfortunately, the majority of decisions about the cost-effectiveness of interventions are also made on an uncertain information.,
Accordingly, communication of clinical benefits and harms is also currently infant, modest limited and in a sorry state. It needs more attention to the theory and practice. There is a lack of transparent method of communicating these information,, and there is a need for better effective clear ways of communicating risk information to patients and healthcare practitioners.,, There is a remained space for development in this area., More effective ways should be developed for clinicians to understand and interpret clinical data and to assess patient perceptions for the harms and benefits of the drugs, devices and biologics that they use. Current ways of how information presented can alter receiver's decisions.,,, Consequently, the lack of shared communication and understanding can increase safety problems.
The Institute of Medicine's committee on the Assessment of the US Drug Safety System recommends that the new Office of Drug Safety Policy and Communication should develop a cohesive risk communication plan to review all risk communication activities of the center, and evaluation of communication tools.
There are also theoretical and practical problems for estimating patient health preferences, such as the proper source of preferences weights. Significantly, different scores may be yielded after the same intervention for the same patients. In spite that current patient wants to contribute actively in his/her treatment and there is an agreement to include patient perception in the outcome, it is not easy to achieve., There is no federal agency, which has a formal method for weighing preference variation, and no consensus in the literature how to do it., It is obvious that both patient outcomes and preferences are often inadequately measured, and consumers and patients are often not sure how to weigh risks and benefits for different options. There is a need for systematic method, which incorporates patients' preferences and values into clinical decisions.
Patient clinical outcome assessment is based primarily on the definition of health and health status. A shared definition of health is needed for valid assessment method and will enhance the quality in health care. The factors of health outcome and what considered as dimensions of health and their relative contributions are unspecified, variable and questionable. The attempts to define health are futile and lack operational value., Health services administrators lack a good working definition of health, and no universally-accepted instruments for measuring it. Quantization the components of health is also a complex task.
WHO defines health as “a state of complete physical, mental and social well-being and not merely the absence of disease or infirmity.” It is not obvious how this definition supports clinical and public health practice or how it can be measured or operationalized., Terms in the definition like “complete”, “social well-being”, and “disease and infirmity” all are not clear and need to be defined. The definition is too abstract and oversimplified. It expresses the final goal in health rather than a method for solid action. It does not illustrate the relative importance of its components, and does not include mortality. It does not distinguish health from happiness, which is not intended to be measured in the health domain for many reasons.
Another common term, which is related to patient clinical outcome assessment, is the Health Related Quality of Life (HRQOL) term. Quality of life has a vague and difficult concept to define. The concept is abstract and complex, and has no definition consensus,,, which reflects the lack of theoretical conceptualization of the term. There are serious methodological and logical troubles in the construction of HRQOL measurement, and it is recommended that HRQOL measurement be neglected. The components of quality of life are a personal issue, which leads to a philosophical rather than a scientific approach.
A good definition could be operationalized and operational definitions of quality of life term are woefully inadequate. The use of the term quality of life to reflect the values and perceptions of patients has confusion, and misunderstanding among health professionals, and patients because of unclear conceptual definition. Evaluation of quality of life is based on arguments rather than on rational debate, and so, comparing instruments on scientific grounds is difficult.
No HRQOL instrument is universally recommended, and no gold standard is available. The differences between quality of life measurement methods highlight the difficulties of a standard definition of the concept. There is not a single instrument, which stands out above the rest, and there are no “worst” or “best” instruments. Accordingly, it is difficult to reflect a decision maker's preference. Lack of standard instrument forms obvious difficulty in the validation of health-related quality of life measures. It is difficult to progress in the field if there is no consensus over concept-definition, and those critical scientific, and logistic obstacles in this field need to be overcome.
Well-being is also a widely used term, which is related to the outcome assessment. Well-being is even more ambiguous. The definitions of the term are diverse and inconsistent, and its vague concept will hamper the development of knowledge and theory in research. Well-being is a complex and many-sided construct, which still eluding researchers to define and measure.
Quality of life and Well-being have a complex construct with variable meaning. They are both elusive concepts having problems in measurement and definition. General accepted definitions for both are still lacking, and they are most often used interchangeably.
An assessment of patient clinical outcome is a very important facet of patient safety, and involves the measurement of the therapeutic options in terms of their benefits, harms and risks. Limited research has been devoted for the development of assessment methods for the clinical field. Consequently, clinical outcome assessment methods in some clinical fields have been employed without sufficient understanding of their characteristics. This triggers the need for more research to better understand the existing patient clinical outcome assessment methods and their conceptual limitations. Accordingly, this paper proposes an informative review on the assessment of patient clinical outcome that will help researchers, clinicians and health care professionals to design novel general methods for the assessment of patient clinical outcome that avoid the conceptual limitations of existing methods.
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Conflicts of interest
There are no conflicts of interest.
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