1.0 Without putting the figures you cited into a spreadsheet, it is quick to appreciate the fact that the medical costs of obesity is clearly higher than the cost of implementing the research project. The net obesity cost (NOC)-to-project implementation cost (PIC) ratio of 10.2 [$1,429 / $140] is convincingly high. A patient’s annual savings from obesity cost can virtually finance this project implementation estimate.
However, it is unclear to us if the cost of implementing the proposed exercise prescription protocol (EPP) or the actual implementation of EPP by the patient, using the necessary equipment and materials to implement the EPP, will be as cost effective. Nevertheless, the cost effectiveness issue apparently goes beyond the cost of implementing the research project (i.e. the cost impact of the proposed EPP to the patients).
Specifically, obesity costs can be higher than the cost of an exercise prescription pad and a pedometer should the steps per day (SPD) measure prove significantly more beneficial than the minutes per day (MPD) measure. However, you apparently missed disclosing the cost of the exercise prescription pad and the pedometer, which the person who will follow should you recommend the use of these equipment for exercise. Thus, we cannot intelligently evaluate if the SPD strategy, which requires a pedometer (and undisclosed other costs), will be more cost effective than MPD, which only needs a reasonably affordable stopwatch.
2.0 A computation of the benefit-to-cost ratio reveals that the economic benefit of your proposed admission prediction tool (APT) for the Emergency Department (ED) of the Long Beach Memorial Medical Center (LBMMC) will be 8.2 times [$3,369,563 / $409,250] more than the cost of installing the APT. This is a convincing economic picture that the APT proposal offers. Indeed, the cost of APT installation is justifiable even if this cost is financed with a loan at prevailing rates. Thus, it will require a far stronger reason for the LBMMC administration to not provide a ‘go’ signal for the implementation of this project.
Perhaps, from the healthcare servicing perspective, the only critical factor in that decision involves the question: How predictive this APT is in anticipating the daily turnouts of patients in the ED and chances of admission with or without a triage support (cf. Sun, Tay, Heng, & Seow, 2011)? It is a fact of ED cases that the emergency medical patients vary widely on a daily basis although always within a certain range. Does the variation narrow enough to be manageable with available human resources? Does it require only a small augmentation of ED staff or a large number of temporary ED personnel are necessary (but financially unsustainable if hired as regular employees) on certain unpredictable days due to high patient turn ups beyond the daily average? Do you already have those figures from LBMMC on a weekly, monthly, and annual bases (at least in the previous two years) to aid you in pinpointing turn ups and admission? Have you used these figures to integrate cost factors in the cost effectiveness analysis?
An excellent APT can analyze these daily data and provide estimates on expected daily turn up of patients in the ED and admission probability without a triage intervention. However, there is always limits to mathematical models, which data analysts must take charge in ensuring that outlier data are not significant and not costly to address from the ED human resources management point of view.
3.0 From the cost analysis you presented, it is easy to agree on the cost effectiveness of the certified registered nurse anesthetist (CRNA) option over the clinical anesthesiologist (CA) option. However, I have the impression that the issue of CRNA vs. CA goes beyond the cost factor although it is a highly meaningful factor from the patient’s perspective. Kokemuller (2016) mentioned about state law requirements and medical association preferences for their member doctors. These are indicators of underlying complications between these differences, which could be significant to service quality and patient care.
Thus, I believe that there must also be included in your research rationale a purely clinical basis for choosing between a CRNA and a CA. From a professional and logical perspective, there must be a sensible justification for differentiating the clinical anesthesiologist specialty from the nursing anesthesiology specialty. Otherwise, either specialty will become functionally redundant in a hospital’s organizational chart. If the CRNA can effectively replace the CA in cases such as surgical anesthesiology, then the CA specialty will have to lose in the comparison, especially among educational and career choices, considering the higher cost of completing a medical study and specializing in clinical anesthesiology.
I recommend that this aspect of the research rationale must be explored thoroughly as part of the argument justifying the study protocol itself, particularly in the area of patient safety and risks, or any other considerations necessary to protect the patient’s life, improve her quality of life, and similar valid consideration for undergoing a surgical treatment. If this aspect cannot be justified convincingly, the benefits from the cost analysis maybe outweighed in the mind of the patient and minimize the decisional value of its cost effectiveness.
4.0 From your calculation, the cost ratio for depressed elderly over non-depressed elderly is 1.2 [$14,365 / $11,956]. That is 20 percent more than double the costs incurred of the elderly without depression. Beyond economic costs, the burden of depression is also convincing enough to do something in lifting up depressing among the elderly. In fact, should the elderly have to go when their time comes, it should be joy and a smile in their lips (cf. Rosenblatt, 2013) not with depression and a whimper. Thus, it is the community’s obligation to ensure that our elderly will enjoy the remaining days of their lives. However, some reports indicate that things had not been that depressing at all for the elderly in general. The Sarasota Herald Tribune reported a few years earlier that a Harvard Grant study showed people have grown happier today as they age (Smith, 2013).
With that in mind, the options for depression intervention for the elderly, whether preventive or therapeutic, must be focused on groups that allow them to positively engage each other in fun and meaningful activities (Rosenblatt, 2013; Smith, 2013), particularly among those physically mobile ones (perhaps the less mobile elderly must be organized differently from the more mobile ones to ensure commonality and same-level engagement), in addition to group processing of depressing issues.
Finding, and even creating, a support group like this will be worth the effort and the cost whatever that might be. Thus, at this point in your proposed study, I suggest that you seek these more proactive groups to consider as options for your study.
5.0 Medical errors are persistent problems, which automation should have corrected but somehow, and to a certain extent, did not. In fact, in the United States, it is the third largest mortality cause (BMJ, 2016). And I agree with you that, despite years of technological innovation, translating research findings into practice can be difficult as the Centre for Evidence-Based Medicine (CEBM) observed. Healthcare systems intended to use information technology to avoid medical errors from physician order entry to personal health records keeping (Agrawal, 2009). And yet medical errors seem to persist still.
Nevertheless, although CEBM mentioned the patient factor in these medical errors, such factor must be considered outside professional control, particularly outside institutional premises, and maybe treated in the study as one of its limitations as far as intervening for medical errors is concerned. Of course, that is based on the extent I understand the focus of your research project. Nevertheless, the study focus must be on factors that healthcare institutions can reasonably control within the institutions themselves and within their legal jurisdictions.
Intuitively, the quality points involve healthcare staff factors because they are crucial conduits between the patients and the technological aspect of processing patient data, which may factor in the emergence of errors in the diagnosis and management of the disease while the patient stays in the healthcare institution. Human mediate occurs from the point of interaction with the patient, obtaining data necessary in diagnosing and managing the patient’s disease, to the point of putting these data on electronic records for analysis and archival objectives. At these points of contact between the patients, the healthcare staff, and the electronic terminal, human action intervenes, which is essentially prone to errors, and even inevitably so (BMJ, 2016), which results to adverse medical errors. In my view, the cost effectiveness analysis must address both the human and technological factors of medical errors.
6.0 In my view, to assume that clinicians took a career in healthcare services on the altruistic ground of helping patient is oversimplifying human motivations. It is intuitively illogical to assume that working in the service industry is driven by a desire to serve more than the economic and prestige (or other personal) objectives, particularly available in the healthcare careers (Girasek, Molnar, Eke, & Szocska, 2011). Problems in patient care, both in service quality and outcomes, are indicators of different drivers beyond the motivation to passionately serve the patients. Passion to serve begets a passion for perfectionist clinical and healthcare servicing, which can more than override potential medical errors and fatal consequences to the patient. If the U.S. healthcare system failed, it indicates that a formidably large number of human actors within that system are not motivated or passionate enough to provide truly safe and error free healthcare services. Thus, if a healthcare system fails, it was the motivation to serve that patients, which failed.
Perhaps it will be an interesting focus to closely investigate the perceived reasons for the resistance to change among healthcare actors. Is it really a problem with “deficit time and money”? Or, is it a problem of motivation? Are these resistors really motivated to serve patient interests? Or, are they simply motivated with their personal “time and money”? These questions allude back to the matter of the providers’ passion to serve the patients as the basis or non-basis of their decision to choose a career in healthcare. With that passion intact, time and money issues are mere obstacles that can be overcome with resolve. Without that passion intact, there will be no resolution available in the first place to even lift a finger over the issue of time and money.
7.0 I agree with Kleinpell’s observation (as cited) that causality is difficult to establish. In fact, it may be added that it is impossible to establish unless an assumption is made, which in itself introduce an uncertainty that has to be assumed otherwise (Pearl, 2009). From a philosophical perspective, causality is a definite and certainty concept: a cause definitely and certainly brings about the effect. This concept is straightforward. Thus, there is no problem in the causality concept. The problem, however, resides in the measurement where uncertainty rises. How can we measure the effect that is exactly and exclusively due to the cause? Measuring that is not possible. Even if statistical mathematics introduced the concept of probability to establish an approximation of the indefinite conditions, probability is still an uncertain estimate.
The concept of randomization was introduced in an attempt to mimic the indefiniteness of the conditions evolving around a causal event. If population samples have all the chances to be selected for the study, then it ‘must’ have mimicked the indefinite condition surrounding the causal event. It ‘assumes’ that indefinite is equivalent to random. However, randomization, like probability, is a mere estimate of the indefinite without clearly knowing if the mimicry had accurately mimicked the exact causal conditions (e.g. all unknown relevant and confounding factors accounted for). It cannot account for factors that were not actually represented in the randomized samples even if it assumes it does. It will not even know which factors it missed.
Thus, any researcher must embrace the fact that research results and findings can only estimate the causal event (Pearl, 2009). It cannot accurately and completely define or understand it. There will always be important factors missed in the process. Recommendations can be made only based on factors uncovered and nothing more. Thus, any implementation will always miss factors that, if incorporated, could contribute beneficially but had not been uncovered in the study. It is an imperfect world and even empirical studies cannot make perfection out of it.
8.0 Sometimes it is fascinating to realize that medical errors do not necessarily translate to lack quality of care. That is exactly my impression with the IOM findings (as cited) and the idea that the perception of quality care goes beyond medical errors and also involves the human factor. However, if we take the IOM findings without a grain of salt, there seems to be no human factor in medical errors. It is all “faulty systems and processes”, not individuals. (Blame it to the programmers instead; not to the healthcare professionals.) If we take this at face value, then it is easy to conclude that automation is a big operational mistake, failing to correct the human errors it was intended to correct at the onset (Agrawal, 2009). Apparently, based on the IOM findings, it is the human beings who successfully took care of eradicating their errors while the machines failed to eradicate theirs. Thus, is IOM recommending that we remove automation and restore the traditional human hands-on system? Admittedly, automation speeds up exponentially the operational processes. However, it will not make sense to use exponential speed when the output data are unpredictably erroneous. And, if we are talking about healthcare services and patient safety, the cost to the patient is very high.
Perhaps, there is something more to the IOM findings. Perhaps, this underlying question can turn into an interesting subject to investigate. Are human healthcare personnel no longer committing medical errors? The British Medical Journal could not agree to that implication (BMJ, 2016). Medication errors committed in the United States alone cannot support such optimism (Agrawal, 2009). Moreover, how far has human beings actually managed to eradicate human error in the healthcare setting? That too is an interesting area to look into. Admittedly, medical error is a complex phenomenon.
References
Agrawal, A. (2009). Medication errors: Prevention using information technology systems.
British Journal of Clinical Pharmacology, 67(6), 681-686.
BMJ. (2016, May 4). Medical error is third biggest cause of death in the U.S., says experts.
British Medical Journal. Retrieved from: http://www.bmj.com/company/wp-content/uploads/2016/05/medical-errors.pdf.
Girasek, E., Molnar, R., Eke, E., & Szocska, M. (2011, August). The medical career choice
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Jacobsen, F. & De Bree, H.-E. (2005). A comparison of two different sound intensity
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Kokemuller, N. (2016). Anesthetist vs. anesthesiologist. Chron.com. Retrieved from:
http://work.chron.com/anesthetist-vs-anesthesiologist-6475.html <12 August 2016>
Sun, Y., Tay, S.-Y., Heng, B.H., & Seow, E. (2011). Predicting hospital admission at emergency
