...Hospital Sentinel Event Report Program & Start Date: M.B.A. Health Care Management WGU A1: Sentinel Event Report Minor child, Tina, had a minor operation and as told to child’s mother per Nightingale Memorial Hospital (NMH) pre-op nurse, the operation duration would be 45 minutes plus an additional hour in recovery. Under the instruction of the patient’s mother, the pre-op nurse was to contact her by cell phone if times for release had changed. Mother arrived approximately 2.5 hours later to pick up patient and found that her daughter had been released 30 minutes prior to her arrival. At that point security issued a “Code Pink” (hospital-wide child abduction alert) and local law enforcement was contacted as well. Upon interviewing the patient’s mother, she stated she has custody of the patient and siblings as the parents are currently divorced. Within 30 minutes of mother’s post-op arrival, local law enforcement confirmed the child was in care of her father. Furthermore, the father and child were located at father’s residence awaiting the arrival of the mother. The father was not charged of any crime. Furthermore, NMH CEO assured patient’s mother that the incident would be investigated and changes to policies may be implemented to prevent similar instances in the future. A2: Personnel Staff below in the order in which they made contact with the said sentinel event: Katie Jessup, Registrar: Ms. Jessup stated that...
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...Organizational Systems and Quality Leadership Task 2 A. Root Cause Analysis The purpose of this root cause analysis is to carefully examine the causative factors, errors, and hazards that led to the sentinel event of Mr. B’s death. Mr. B was 67 year old male that presented to the ED with his son and neighbor. Mr. B stated that he tripped and fell over his dog. Upon assessment Mr. B’s vital signs were stable with the exception of rapid respirations, his left leg was shortened, red and swollen, and pain was rated 10 on 1-10 scale. The first step to a root cause analysis is to identify what happened. In this scenario, the patient is admitted to an ED room after proper triage. Initially, 5 mg diazepam was ordered by the ED Doctor, and administered by Nurse J at 4:05 pm. After five minutes, no status change is noted and Dr. T ordered 2 mg hydromorphone IVP. This is administered at 4:15 pm. Dr. T then ordered an additional 2 mg hydromorphone IVP, as well as an additional 5 mg diazepam IVP. At 4:25 pm a successful reduction of left hip takes place. At 4:30 the procedure is concluded. The patient remained sedated, showing no signs of distress or discomfort. Mr. B is not currently on supplemental oxygen. Mr. B is placed on an automatic blood pressure machine programmed to monitor the blood pressure every five minutes as well as a pulse oximeter. At 4:35 pm Mr. B blood pressure is 110/62 and O2 saturation is 92%. He has no supplemental oxygen, and his ECG and respirations...
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...Medication error, a word that can have hospital staff quaking in their shoes, skin pallor, and hair prickling; leaving a client to gather the pieces after a miscalculated error. This type of event is termed; sentinel event. According to Taylor’s Fundamentals of Nursing, “The Joint Commission’s Sentinel Event Policy defines a sentinel event as an unexpected occurrence involving death or serious physical or psychological injury, or the risk thereof (129).” Suddenly, Florence Nightingale’s oath shatters because staff “[…] knowingly administer a harmful drug” containing contaminants, potentially diminishing the patient’s quality of life. How could this happen? Using only one insulin pen between multiple patients. Sentinel Event Case: Gordon Hayes There are numerous instances of pharmacological sentinel events involving insulin ordinarily a high alert medication. One instance is accidental or intentional use of an insulin pen amongst multiple patients; situating them at risk for blood-borne pathogens. This type of medication error necessitates prevention before reaching the patient. Gordon Hayes a seventy-three-year-old who lives in Ansonia,...
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...The Community’s Effect on Education Of all the problems that society faces today, perhaps the worst is the declining quality of the education of our children. Education plays a major part in the life of a child: a quality education allows for a child to be exposed to more opportunities that can lead to a quality of living that, in a lot of cases, is much better than that of their parents. There are many ways to increase the quality of a child’s education; however, the best way is for the community, and society at large, to take an interest and become involved. According to Robert Putnam, a major issue facing society is the difficulty of creating “bridging social capital.” This is created when people of different groups and backgrounds come together to improve their community and is important for maintaining a healthy public life in a world that is becoming increasing more diverse. The difficulty arises from the problems that come out of trying to bridge the many groups that have little to no commonalities. I believe that the service learning that we are doing through the Burnett Honors College is addressing this issue by making bridging social capital. We, the college students, come from different backgrounds than the children we are sent to work with. We are bridging the gap by showing them through face-to-face interactions that they too can go to college. I think that the work we are doing is important because we are helping to improve the quality of these children’s...
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...Unit 2 DB Subjective Probability “ A probability derived from an individual's personal judgment about whether a specific outcome is likely to occur. Subjective probabilities contain no formal calculations and only reflect the subject's opinions and past experience.” (investopedia.com, 2013) There are three elements of a probability which combine to equal a result. There is the experiment ,the sample space and the event (Editorial board, 2012). In this case the class is the experiment because the process of attempting it will result in a grade which could vary from an A to F. The different grades that can be achieved in the class are the sample space. The event or outcome is the grade that will be received at the end of the experiment. I would like to achieve an “A” in this class but due to my lack of experience in statistical analysis, my hesitation towards advanced mathematics, and the length of time it takes for me to complete my course work a C in this class may be my best result. I have a 1/9 chance or probability to receive an “A” in the data range presented to me which is (A,A-,B,B-,C,C-,D,D- AND F). By the grades that have been posted I would say that the other students have a much better chance of receiving a better grade than mine. I have personally use subjective probability in my security guard business in bidding on contracts based on the clients involved , the rates that I charge versus the rates other companies charge and the amount of work involved...
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... Probability – the chance that an uncertain event will occur (always between 0 and 1) Impossible Event – an event that has no chance of occurring (probability = 0) Certain Event – an event that is sure to occur (probability = 1) Assessing Probability probability of occurrence= probability of occurrence based on a combination of an individual’s past experience, personal opinion, and analysis of a particular situation Events Simple event An event described by a single characteristic Joint event An event described by two or more characteristics Complement of an event A , All events that are not part of event A The Sample Space is the collection of all possible events Simple Probability refers to the probability of a simple event. Joint Probability refers to the probability of an occurrence of two or more events. ex. P(Jan. and Wed.) Mutually exclusive events is the Events that cannot occur simultaneously Example: Randomly choosing a day from 2010 A = day in January; B = day in February Events A and B are mutually exclusive Collectively exhaustive events One of the events must occur the set of events covers the entire sample space Computing Joint and Marginal Probabilities The probability of a joint event, A and B: Computing a marginal (or simple) probability: Probability is the numerical measure of the likelihood that an event will occur The probability of any event must be between 0 and 1, inclusively The sum of the...
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...= {-20, -19, …, -1, 0, 1, …, 19, 20} Number of people arriving at a bank in a day: S = {0, 1, 2, …} Inspection of parts till one defective part is found: S = {d, gd, ggd, gggd, …} Temperature of a place with a knowledge that it ranges between 10 degrees and 50 degrees: S = {any value between 10 to 50} Speed of a train at a given time, with no other additional information: S = {any value between 0 to infinity} 4 Sample Space (cont…) Discrete sample space: One that contains either finite or countable infinite set of outcomes • Out of the previous examples, which ones are discrete sample spaces??? Continuous sample space: One that contains an interval of real numbers. The interval can be either finite or infinite 5 Events A collection of certain sample points A subset of the sample space Denoted by ‘E’ Examples: • Getting an odd number in dice throwing experiment S = {1, 2, 3, 4,...
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...the stage where one can begin to use probabilistic ideas in statistical inference and modelling, and the study of stochastic processes. Probability axioms. Conditional probability and independence. Discrete random variables and their distributions. Continuous distributions. Joint distributions. Independence. Expectations. Mean, variance, covariance, correlation. Limiting distributions. The syllabus is as follows: 1. Basic notions of probability. Sample spaces, events, relative frequency, probability axioms. 2. Finite sample spaces. Methods of enumeration. Combinatorial probability. 3. Conditional probability. Theorem of total probability. Bayes theorem. 4. Independence of two events. Mutual independence of n events. Sampling with and without replacement. 5. Random variables. Univariate distributions - discrete, continuous, mixed. Standard distributions - hypergeometric, binomial, geometric, Poisson, uniform, normal, exponential. Probability mass function, density function, distribution function. Probabilities of events in terms of random variables. 6. Transformations of a single random variable. Mean, variance, median, quantiles. 7. Joint distribution of two random variables. Marginal and conditional distributions. Independence. iii iv 8. Covariance, correlation. Means and variances of linear functions of random variables. 9. Limiting distributions in the Binomial case. These course notes explain the naterial in the syllabus. They have been “fieldtested” on the class of 2000...
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...[pic] [pic] Markov Chain [pic] Bonus Malus Model [pic] [pic] This table justifies the matrix above: | | | |Next state | | | |State |Premium |0 Claims |1 Claim |2 Claims |[pic]Claims | |1 | |1 |2 |3 |4 | |2 | |1 |3 |4 |4 | |3 | |2 |4 |4 |4 | |4 | |3 |4 |4 |4 | | | | | | | | |P11 |P12 |P13 |P14 | | | |P21 |P22 |P23 |P24 | | | |P31 |P32 |P33 |P34 | | | |P41 |P42 |P43 |P44 | | | | ...
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...Permutations The word ‘coincidence’ is defined as an event that might have been arranged though it was accidental in actuality. Most of us perceive life as a set of coincidences that lead us to pre-destined conclusions despite believing in a being who is free from the shackles of time and space. The question is that a being, for whom time and space would be nothing more than two more dimensions, wouldn’t it be rather disparaging to throw events out randomly and witness how the history unfolds (as a mere spectator)? Did He really arrange the events such that there is nothing accidental about their occurrence? Or are all the lives of all the living beings merely a result of a set of events that unfolded one after another without there being a chronological order? To arrive at satisfactory answers to above questions we must steer this discourse towards the concept of conditional probability. That is the chance of something to happen given that an event has already happened. Though, the prior event need not to be related to the succeeding one but must be essential for it occurrence. Our minds as I believe are evolved enough to analyze a story and identify the point in time where the story has originated or the set of events that must have happened to ensure the specific conclusion of the story. To simplify the conundrum let us assume a hypothetical scenario where a man just became a pioneer in the field of actuarial science. Imagine him telling us his story in reverse. “I became...
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...presence with probability 0.99. If it is not present, the radar falsely registers an aircraft presence with probability 0.10. We assume that an aircraft is present with probability 0.05. What is the probability of false alarm (a false indication of aircraft presence), and the probability of missed detection (nothing registers, even though an aircraft is present)? A sequential representation of the sample space is appropriate here, as shown in Fig. 1. Figure 1: Sequential description of the sample space for the radar detection problem Solution: Let A and B be the events A={an aircraft is present}, B={the radar registers an aircraft presence}, and consider also their complements Ac={an aircraft is not present}, Bc={the radar does not register an aircraft presence}. The given probabilities are recorded along the corresponding branches of the tree describing the sample space, as shown in Fig. 1. Each event of interest corresponds to a leaf of the tree and its probability is equal to the product of the probabilities associated with the branches in a path from the root to the corresponding leaf. The desired probabilities of false alarm and missed detection are P(false alarm)=P(Ac∩B)=P(Ac)P(B|Ac)=0.95∙0.10=0.095, P(missed detection)=P(A∩Bc)=P(A)P(Bc|A)=0.05∙0.01=0.0005. Application of Bayes` rule in this problem. We are given that P(A)=0.05, P(B|A)=0.99, P(B|Ac)=0.1. Applying Bayes’ rule, with A1=A and A2=Ac, we obtain P(aircraft present | radar registers) =...
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...1.M/G/ Queue a. Show that Let A(t) : Number of arrivals between time (0, t] “ n should be equal to or great than k” since if n is less than k (n<k), Pk(t)=0 Let’s think some customer C, Let’s find P{C arrived at time x and in service at time t | x=(0,t)] } P{C arrives in (x, x+dx] | C arrives in (0, t] }P{C is in service | C arrives at x, and x = (0,t] } Since theorem of Poisson Process, The theorem is that Given that N(t) =n, the n arrival times S1, S2, …Sn have the same distribution as the order statistics corresponding to n independent random variables uniformly distributed on the interval (0, t) Thus, P{C is in service | C arrives between time (0, t] } Since let y=t-x, x=0 → y=t, x=t →y=o, dy=-dx Therefore, In conclusion, ------ (1) 1-a Solution Since b. let 1-b Solution ------------------------------------------------- 2. notation Page 147 in “Fundamentals of Queuing Theory –Third Edition- , Donald Gross Carl M. Harris a. b. ------------------------------------------------- ------------------------------------------------- ------------------------------------------------- 3. a. let X=service time (Random variable) and XT=total service time (Random variable) X2=X+X, X3=X+X+X, ….. f2(x2)...
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...Probability & Mathematical Statistics | “The frequency concept of Probability” | [Type the author name] | What is probability & Mathematical Statistics? It is the mathematical machinery necessary to answer questions about uncertain events. Where scientists, engineers and so forth need to make results and findings to these uncertain events precise... Random experiment “A random experiment is an experiment, trial, or observation that can be repeated numerous times under the same conditions... It must in no way be affected by any previous outcome and cannot be predicted with certainty.” i.e. it is uncertain (we don’t know ahead of time what the answer will be) and repeatable (ideally).The sample space is the set containing all possible outcomes from a random experiment. Often called S. (In set theory this is usually called U, but it’s the same thing) Discrete probability Finite Probability This is where there are only finitely many possible outcomes. Moreover, many of these outcomes will mostly be where all the outcomes are equally likely, that is, uniform finite probability. An example of such a thing is where a fair cubical die is tossed. It will come up with one of the six outcomes 1, 2, 3, 4, 5, or 6, and each with the same probability. Another example is where a fair coin is flipped. It will come up with one of the two outcomes H or T. Terminology and notation. We’ll call the tossing of a die a trial or an experiment. Where we...
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...Model Answers for Chapter 4: Evaluating Classification and Predictive Performance Answer to 4.3.a: Leftmost bar: If we take the 10% "most probable 1’s(frauds)” (as ranked by the model), it will yield 6.5 times as many 1’s (frauds), as would a random selection of 10% of the records. 2nd bar from left: If we take the second highest decile (10%) of records that are ranked by the model as “the most probable 1’s (frauds ” it will yield 2.7 times as many 1’s (frauds), as would a random selection of 10 % of the records. Answer to 4.3.b: Consider a tax authority that wants to allocate their resources for investigating firms that are most likely to submit fraudulent tax returns. Suppose that there are resources for auditing only 10% of firms. Rather than taking a random sample, they can select the top 10% of firms that are predicted to be most likely to report fraudulently (according to the decile chart). Or, to preserve the principle that anyone might be audited, they can establish differential probabilities for being sampled -- those in the top deciles being much more likely to be audited. . Answer to 4.3.c: Classification Confusion Matrix Predicted Class 1 (Fraudulent) Actual Class 1 (Fraudulent) 0 (Non-fraudulent) Error rate = 0 (Non-fraudulent) 30 58 32 920 n0,1 + n1,0 32 + 58 = = 0.0865 = 8.65% n 1040 Our classification confusion matrix becomes Classification Confusion Matrix Predicted Class 1 (Fraudulent) ...
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...Memorandum To: CC: From: Date: Re: The Cincinnati Enquirer Kristen DelGuzzi Ashley N. Ear; September 16, 2007 Data Analysis of Hamilton County Judges Probabilities used to assist with Ranking of Hamilton County Judges After the current statistics were gathered to produce data analysis regarding Hamilton County Judges, we can come to a conclusion and rank judges appropriately by their probability to be appealed, reversed and a combination of the both. With the provided data analysis, I have included statistics to all probabilities including: total cases disposed, appealed cases, reversed cases, probability of appeal, rank by probability of appeal, probability of reversal, rank by probability of reversal, conditional probability of reversal given appeal, rank by conditional probability of reversal given appeal and overall sum of ranks. The judges that rank the highest (i.e. 1st, 2nd, 3rd) have the lowest probability to have appealed cases, reversed cases and lowest conditional probability of reversed cases given appeal. In my opinion, by ranking the judges as such, we can see how often their ruling is upheld, which is ultimately desirable when concerning the credibility of a judge. I have provided rankings for all three different courts including: Common Pleas Court, Domestic Relations Court and Municipal Court. These overall rankings are gathered by summing up all of the rankings by the three probability variables. I have also provided data analysis which interprets who...
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