The Hypothetico-Conductive Model Of Diagnostic Reasoning

2. Which patient findings/observations lead you to your primary diagnosis? How do they relate to the primary diagnosis? (1 point)

Nurses are the first to assess patients that are seeking health care services. Nurses are the Frontline Warriors who risk their own lives to provide care for others (Ungar, 2014). They are the Frontline Warriors who diagnose the care of patients to ensure doctor orders are accurate before care is practiced. A recent study estimates 12 million Americans are misdiagnose every year and 6 million results in serious outcome (Singh, Meyer & Thomas, 2013). Without effective communication and quality management protocols, misdiagnoses may cause possible harm to patients, healthcare workers, and the population. For instance, on September 24, 2014, Thomas Duncan, a man from Liberia in his early 40’s went to Texas Health

Concerns about AI also encompass whether it does create the most accurate diagnoses for patient care. Since AI uses large data banks to make decisions for what treatments may be best for a patient or for a diagnosis of an illness, people may be led to believe that this option is less personalized. Some people have made this belief since AI takes information from the general public to make a decision about an individual. However, research has shown that the accuracy of diagnoses when using data banks to make medical decisions is far more accurate than a diagnosis from a typical doctor. Also, doctors normally do not have the same opinions so that creates inconsistency for diagnoses, whereas AI would be consistent (Sissons, Ben. “Using Artificial Intelligence to Bring Evidence-Based Medicine a Step Closer to Making a Difference”). The average doctor gives correct diagnoses less than half the time. The accuracy for different diagnoses depend on the field of study. For example, the accuracy of AI for correctly diagnosing dermatological diseases was 97.55%. The accuracy for diagnosing a group of people with a certain type of diabetes came to be 79.37%, which is still higher than the average for a typical doctor (Lekkas, Stavros. “Evolving Fuzzy Medical Diagnosis of Pima Indians Diabetes and of Dermatological Diseases”). This research shows that AI is capable of making accurate diagnoses. Ethical concerns were

Initially, the professor’s assignment provides a unique view on what normal diagnostic tests usually are, and the instructions for his assignment allow me to think of what I would write as a

This mode is particularly useful in helping the user select the clinical manifestations that are important without forcing them to enter a large number of less relevant findings. At any time in this mode, the user can interrupt DXplain to ask “Why?” i.e. to ask DXplain to justify why this particular clinical manifestation is important. DXplain will respond by displaying the name of the disease that is being considered at that point in the interaction and the reason the particular finding might be important in confirming the presence of that disease. DXplain’s ability to explain and justify are key elements of the system. It is critical that this system not be perceived as a magic black box that can somehow provide the “answer” to a complex diagnostic problem. We believe that physicians will not accept DXplain as a useful diagnostic assistant unless the clinical interpretations seem reasonable and unless the system can offer explanations that are understandable and persuasive. (Barnett et al.,

This review is to discuss an overview of this case study with a clinical reasoning model and all contributing factors of this event. Then, the critical analysis of three articles relating to the factors with the reasons for the selection and their evaluation will be presented.

The availability heuristic is the tendency to consider events more common than they really are if they come to mind quickly or easily (Croskerry, 2002). Though the recency of seeing a disorder does not increase the base rate of its occurrence, faulty cognitions result in providers thinking this way. This bias appeared in a study exploring how likely a doctor was to say that a patient had bacteremia. In this study, the doctors judged this disease as much more probable if they had recent experience treating it, despite the base rate of infection being the same in either case. What first came to mind was not necessarily most accurate, but most salient (Klein, 2005). Particularly affected are specialized practices. Because a provider sees a specific disorder frequently, it is commonly most salient and more likely to lead to the availability bias. This heuristics poses a particular threat in ambiguous cases; the diagnosis received may depend solely on which provider evaluated the patient first (Saposnik, Redelmeier, Ruff, & Tobler,

Ambiguity and lack of regulatory control is echoed in David Powner’s article published in the Lancet Journal: “Clinicians do not always follow an established policy or provide appropriate documentation. Surveys or chart reviews showed that doctors sometimes failed to document specifics of clinical examinations, omitted criteria demanded

Differential diagnoses are developed by a clinician upon learning of the chief complaint. One must begin to develop the possibility of potential diagnoses mentally to guide the care provided to the patient. These potential diagnoses are developed by the care provider and are often based on one’s past clinical experiences, awareness of the illness and a clear understanding of the patient’s complaint (Goolsby & Grubbs, 2014). The care provider with experience may develop these diagnoses independently and others with less experience may utilize evidence-based resources and clinical guidelines to aid in this process (Goolsby & Grubbs, 2014). The process for reaching a final diagnosis requires further investigation and use of physical assessment

A radiographer would assess the request to confirm it was justified before commencing the exam. Clear communication of the clinical evidence from the referring clinician is necessary, and the implications of ambiguous or incorrect information may result in delays to the patient pathway or unnecessary imaging being performed. This need for the radiographer to make clear and informed decisions within the multidisciplinary team shows a necessity to utilise lifelong learning to both maintain specialist knowledge with clinical skills and problem solving, and the need to engage in interprofessional learning and working (Barrett et al., 2005,

If a patient makes an enquiry and you are unsure of it is better to seek the correct information from a colleague than to give the wrong information.

Most people cannot be diagnosed by just looking at them, although it is not impossible to do. Some doctors and nurses can look at a person and come up with a list of diagnosis and act accordingly. A diagnosis may be predictable at times but most time diagnoses are totally unexpected. From the four people introduced two of their diagnosis were surprising and the last two not so much. Person A has Dementia and Person D has a self-inflicted abdominal wound, which could be easily explained because Person A is 88 years old. Person D diagnosis is less surprising because he is a man charged with a crime he say he did not commit, so his self-inflicted wound derived from desperation. On the other hand person B and C medical diagnosis are shocking.

Clinical reasoning can be defined as, ‘the process by which nurses (and other clinicians) collect cues, process the information, come to an understanding of a patient’s problem or situation, plan and implement interventions, evaluate outcomes and reflect on and learn from the process’ (Levett-Jones & Hoffman 2013, p.4). It requires health professionals to be able to think critically and ensures better engagement and results for the patient (Tanner 2006, p.209). The Quality in Australian Healthcare Study (Wilson 1995, p.460) discovered that ‘cognitive failure’ resulted in approximately 57% of unfavourable clinical events involving the failure to produce and act correctly on clinical information. It also recognises that often nurse’s preconceptions and assumptions can greatly affect patient care and by going through such a process, one can take into account the holistic nature of the patient and provide the best, most appropriate care.

A therapeutic specialist won't have the capacity to legitimately treat his patient on the off chance that he won't know for beyond any doubt what his patient's disease is. This is the reason the part of pathology experts is critical in the conclusion and treatment of patients.

Mukherjee (2015) talks about the three laws of medicine however these are his personal laws that may or may not be followed by other health professionals. He explains each law that he had learned through personal experiences with patients. The first law is ‘A strong intuition is much more powerful than a weak test,’ explains that there may be some hidden variable when diagnosing a patient that could be crucial in life or death situations. A variable could be the environment that a person lives in or their lifestyle and this chapter notes to know when to look for small clues that could possibly help. The second law is ‘”Normals” teach us rules; “outliers” teach us laws, ' talks about how normal cases teach and build the rules of what should be done on a regular basis of patients, what is normal. Outliers are the cases where it may untreatable but has the chance to reshape and even advance medicine. The third law is 'For every perfect medical experiment, there is a perfect human bias, ' talks about how we hope for a medical treatment that can help treat a disease but it is biased because it either works or doesn’t work despite a few anomalies. These are laws Mukherjee has learned from experience and applies throughout his career, they may not be followed by all health practitioners.