need explanation for the following.

\fBiopsy probability is Likelihood that followup treatment has falsepositive outcome. Early stage suryiyal probability 5E Likelihood of suryiyal it cancer is detected early [i.e.r by the screening test}. Late stage survival probability EL Likelihood of survival it cancer not detected by screening or no Screening is done. Cancer death utility U1 Utility that the consumer assigns to cancer treatment 34 death. Falsepositive utility U2 Utility that the consumer assigns to falsepositive outcome. Maleyeff and Chen 2881 Equation (3") shows the ratio of utilities. that would cause an indiyidual to accept cancer Screening based on the mammogram performance data illustrated in the Appendix, where the falsepositiye outcome is an unnecessary biopsy. That is, it the individual's utility ratio exceeds 512, then cancer screening would be accepted; otherwise, cancer screening would be declined. Title Write the title of the article in AP. Format. Summary Provide a summary of the article you are critiquing: this shows you understood it. Provide a brief overview of what the article was trying to do {i.e., the problem}, methods, if relevant, and the thesis or findings. Make sure to mention the title and author's name. Critical Evaluation Write your critical evaluation of the article. You might address the article's problem, methods, orfindings, or all of these, offering specific support from the text itself {using paraphrase or direct quote and indicating the page number} for your observations. Below are some guiding questions to help you think of what to write: Did the article ask the right question in the first place? How does it fit with other articles on the same topic? Did it miss any important studies it should have considered? Did the problem match the methods? For example, to understand student behavior, were students observed or interviewed, or did all the data come from teachers? Were the findings reported in a consistent and clear format? Did you notice problems in the data that the article overlooked? Did the article fail to acknowledge and explain any limitations? Was the logic clear and were claims properly supported with convincing data? Did you spot any fallacies? Opinion Write your opinion of the article. Did you agree with thethesis or believe the findings? If everything was logical, clear, and wellordered, yet you remain skeptical, how would you explain that? Perhaps a fundamental difference in values would explain it, or perhaps you know of counterevidence not considered by the author. Contribution to Knowledge Write your estimation of the article's contribution to knowledge and its implications or applications to our world. ARTICLE 2 Consumer health informatics approach for personalized cancer screening decisions using utility functions John Maleyeff Metropolitan College, BostonLJniversity, U533. Danrong ChenCollege of Arts EL Sciences, Boston University, [.1331 Abstra ct A consumer health informatics approach is used to investigate the development ofa patientcentered decision support system {DES} with individualized utility functions. It supports medical decisions that have uncertain benefits and potential harms. Its use for accepting or declining cancer screening is illustrated. The system's underlying optimization model incorporates two userspecific utility functionsone that guantifies lifesaving benefits and one that guantifies harms, such as unnecessary follow-up tests, surgeries, or treatments. The system requires sound decision making. Therefore, the decision making process was studied using a decision aid in the form ofa colorcoded matrix with the potential outcomes randomly placed in proportion to their likelihoods. Data were collected from 48 study participants, based on a central composite experimental design. The results show that the DSS can be effective, but health consumers may not be rational decision makers. Keywords cancer screening, clinical decision making, consumer health informatics, ehealth, health utility functions, medical decision modeling The American Medical Informatics association describes consumer health informatics {CHI} as focusing on \"information structures and processes that empower consumers to manage their own healthfor example health information literacy, consumerfriendly language, personal health records, and Internet-based strategies and resources'tl Although its core competencies Original Article ZBTE Health Informatics Journal 26(4} are difficult to specify,2,3 it is clear that CHI approaches can be used to promote shared patientphysician decision making.48 They can also be used for home health applications';a and to assist in the implementation of P4 medicine by creating applications that are personalized and participatory, while promoting proactive and preventative health choices.1},11 The CHI application described below concerns patientcentered clinical decision making for medical procedures that are subject to uncertainties. Although it focuses on cancer screening, the methods apply to chronic disease treatmentsr pharmaceutical drugs, and elective surgery. The aim of the research was to investigate the potential for a patientcentered decision support system [DSSJ that uses an individualized utility function to support medical decisions that have uncertain benefits and potential harms. The article proceeds as follows. it decision model is developed that optimizes the decision to accept or decline screening based on personalized utility functions. To evaluate its efficacy, a data collection scheme is described that determines individual utilities using a graphical decision aid and a set of controlled hypothesized cancer screening scenarios. Then, results from a group of study participants are presented and discussed. Finally, the ability of health consumers to make rational decisions is analyzed. Background In the guest to empower health consumers to take charge oftheir medical care, certain challenges need to be overcome. They include r"various degrees of health literacy and numeracy, consumers' ability to effectively compare statistical and other information'llZ lEither challenges include a reluctance to choose decisions involving uncertainty, called ambiguity aversion,13 and biases due to the waya physician presents information to consumers.14 For example, the choice to present potential treatments in a positive frame or negative frame has been shown to bias medical decision makers.15 Presenting a plethora of relevant information can be counterproductive, due to the biases caused by certain psychological phenomena, such as cognitive overload and anticipated regret.16 Finally, perceived likelihoods based on an individuals' perception oftheir cohort can cause decision bias.1? The American Cancer Society {AC5} recommends that specified populations undergo routine screening for breast, colon, rectal, and cervical cancer.18 They also recommend that highrisk individu als undertake early detection mechanisms for uterine, lung, and prostate cancer. In many cases, personalized decision are recommended. For example, the ACE recommends that a woman between the ages of 4D and 44 undergo mammography \"ifthey wish to do so." Increasingly, shared decision making is recommended for cancer prevention, screening, and treatmentsJEi In fact, the LLB. Preventive Services Task Force recommends individualized decisions for prostate cancerEO and breast cancer. For many medical professionals and public health organizations, a single r'does it save lives?\" criterion has dominated cancer screening recommendations. A meta-analysis of 5? clinical trials covering multiple cancers found a consistency in the tabulation of benefits, with harms reported inconsistently.22 However, for many consumers, the benefits of cancer screening are small and potential harms need to be taken into account.2325 These harms include adverse side effects and effects of overdiagnosisE For example, it has been reported that annual breast cancer screening saves one life per 1004:} women, but about 500 women will experience a false-positive screening result, 64 will undergo an unnecessary biopsy, and 10 will undergo unnecessary cancertreatment? Improved technologies may increase the decision complexity because, although they can identify more abnormalities, many of these abnormalities will not develop into cancer.28 lifter getting false positive result, individuals may be less likely to undergo future cancer screening.29,3CI A clinical DES can help empower health consumers.31,32 The USS should include a knowledge base, a decision aid, an optimization model, and a methodologythat accounts for individual consumer preferences.33 If properly implemented, it would support decisions of individuals, groups, and organizations.34 It could also be used by public health informatics professionals when applied to developing guidelines for disease prevention.35 The DES should be created by an interdisciplinary team of health informatics experts, medical practitioners, and consumers.36 Models embedded in a D85 to support cancer screening decisions can take many forms, from qualitative rulesbased checklists to artificial intelligence methodologies.19,3? Stochastic [i.e., prob ability-based} decision models have been employed,3E'= including those that address designing a testing system39 and those that apply signal detection theory to image evaluation.4} Although these models include accurate estimates of probabilities. associated with benefits and harms, they can suffer from the inability to quantify impacts effectively. Some models use financial estimates associated with each potential outcome-=11 This approach fails to account for individual preferences and other considerations, such as pain and anxiety. Financial considerations are not be highly relevant to many health consumers because cancer screening is offered at little or no cost. Utility functions have been used to quantify the value an individual places on their mental and physical health by encompassing a myriad of criteria,42 including emotions.43 For example, higher levels of dread, uncertainty, and anxiety increase risk aversion in many individuals-=14 Utility approaches are relevant in cancer screening because preferences differ across consumers-45 Health related utilities are often quantified using the qualityadjusted life year index based on a standard gamble, timetrade off comparisons, and discrete choice experiments.42,4o They can encompass quality of life4?' and temporary health states-48 They have been applied to screening decisions for prostate cancer-=19 and breast cancer. 1!". graphical decision aid can enhance a decision maker's ability to weigh benefits against harms by simultaneously presenting the likelihoods associated with more than one potential outcome. Decision aids can help minimize the bias created by framing clinical options in terms of benefits.52 They can help mitigate an individual's inability to make compare options that include low likelihood but high severity outcomes.53 They minimize the variation in how the same likelihood is perceived across individuals,54 and increase the accuracy of information.55 Methods The scenario shown in Figure 1 represents a generic cancer screening system. The patient under goes the screening test, with either a positive or a negative result. Those patients with a positive result undergo a more advanced followup test, which will identify many falsepositive screening results. Patients with a positive followup test will undergo a biopsy, where the false-positive out come may be detected. Patients with a positive biopsy will undergo treatment (chemotherapy, tumor removal, radiology, etc]. They will survive the cancer or die from the cancer. In some cases, the treatment may have been unnecessary because the malignant tumor would not have caused their death. The probabilistic decision tree shown in the appendix includes both the benefits and harms of cancer screening based on the generic scenario. The numerical probabilities shown as illustration are based on the mammogram performance data referenced above.2? Table 1 summarizes the parameters used in the model and displayed in the decision tree. The two relevant utilities are both cost related. They are: (a) the utility the consumer assigns to cancer treatment and subsequent death {Ulithis likelihood is represented by equation {1}, and {b} the utility the consumer assigns to the overdiagnosis harm associated with a falsepositive outcome [U2}. The false-positive outcome can be specified depending on the stage at which it is identified, such as an inconvenient followup test, an unnecessary biopsy, or unnecessary cancer treatment. assuming that the falsepositive outcome is an unnecessary biopsy, its likelihood is shown as equation {2}. The model will identify the optimal decision based on the expected utilities for each alternative. When the falsepositive outcome is defined as an unnecessary biopsy, the optimal decision is to accept screening when its expected cost is lower, as shown in equations {36}. The ratio of the two utilities dictates the optimal decision. Equation {5} shows the conditions forwhich a person should accept the screening test, assuming that the falsepositive outcome is defined as an unnecessary biopsy. It also shows that the ratio of utilities is equivalent to the ratio of: {a} the probability ofa falsepositive outcomer and {b} the screening tests life saying likelihood. Parameter Notation Description Incidence of cancer p Patientspecific cancer incident likelihood. False negative probability a Likelihood that screening test does not detect cancer {ld is the tests sensitivity}. False-positive probability F3 Likelihood that screening test incorrectly concludes cancer {15 is the tests specificity}