Statistical thinking is one of the most critical inputs within the healthcare environment that influences a wide variety of decisions and their respective outcomes. For instance, statistical thinking is applied to make significant improvements leading to informed decisions that try to solve some of the striking healthcare challenges (Newell, Burnard, & Newell, 2011). Typically, it involves the use of statistical concepts, tools, and techniques, as well as, integrating information technology and other disciplines to achieve improved results. HMO’s Pharmacy is facing a critical challenge with its drug prescription process, characterized by poor quality control checks and heightened exposure of the patients to harm resulting from wrong dosage (Newell et al. 2011). HMO’s Pharmacy drug prescription problem can be solved effectively through process mapping and root-cause analysis concepts that are borrowed from the statistics discipline.
HMO’s Pharmacy Process Map
The HMO’s Pharmacy prescription problem reveals poor quality control and high inaccuracy levels during the prescription process of drugs to patients. Notably, this problem requires a logical reasoning to identify the root cause and perhaps suggest or develop potential solutions (Griffin et al. 2016). Further, using the Supplier, Input, Process steps, Output and Customer (SIPOC) model, it is easier to identify the potential glitches which the pharmacy is facing.
Delegate your assignment to our experts and they will do the rest.
Chart 1
HMO’s Pharmacy Process Map
SIPOC Model Analysis for HMO’s Pharmacy Model
Considering the above presented process map, several aspects can be noted. The key problems for the prescription filing process appears twofold. For instance, there is processing of inaccurate prescriptions to its patients and the other problem is that the pharmacy could be facing longer time to accurately make the correct prescriptions. The wrong prescription may result in from poor dosage instructions that gets the patients to take wrong drugs or the right drugs but with the wrong dosage. Using the SIPOC model, several insights can be identified to assist in identifying the root causes of the above mentioned problems. The chart above depicts a clear process map of the drug prescription process with each step or decision making point being identified under one of the five elements of the model (Griffin et al., 2016). Considering a high-level drug prescription model like the one presented above there is long waiting time for the patients to collect their prescribed drugs within the HMO’s Pharmacy current model.
Causes of the Problems
One of the most root case of the poor or wrong description may be seen with the physician. Considering the current medical and pharmaceutical industries, there are many drugs with almost identical names, especially when they share the same family. Such an aspect tells that a simple aspect such as poor handwriting from the physician would lead to wrong interpretation by the technician at the pharmacy – and this would result in the wrong prescription (Tripathy et al., 2018). The physician in this case is identified as the supplier while the selection of the medical and writing the prescription to the patient are categorized as inputs. The key challenge that could lead to inaccurate prescriptions at this stage as identified in the case is the physician’s sloppy handwriting and incompetent instructions – all that can be categorized under the Input element of the SIPOC model.
On the other hand, the long waiting time for the prescription process can be identified under the Process elements of the SIPOC model. Based on the problems presented by Ben Davis, the HMO’s current model is more traditional in the way it is operated (Weiss & Sutton, 2009). Patients seem to take a long time in the counterchecking medical selection, measuring processes and delivering it to the customer. With the counterchecking process, the assistants need to check with the physician if the prescription is legit since some patients can forge it. Still, the assistant pharmacist has to check with the insurance and ensure that they will get reimbursed since many patients are copays. When everything is ok, the pharmacist start processing the orders as patients wait for their completed prescriptions. Furthermore, the pharmacists have the mandate to go through the side effects of the drugs with the patients before handing them over as they countercheck their ID’s to ensure that every patient picks the right prescriptions. The overall effect is that all this process is manual and time-consuming.
Another potential root cause of the wrong prescription may occur with the selection and measuring processes. During the selection of the medicine under prescription, the pharmacist should be extra careful with the names of the drugs under the prescription label, since some drugs may have identical names (Tripathy et al. 2018). Further, the measuring process of the medication may also be a potential root cause of wrong prescriptions. One common cause of prescribing inaccurate dosages may be use of abbreviations especially when labeling drugs since it may lead to poor or wrong interpretation by the individual dispensing the drug.
The main causes of the prescription problems explaining HMO’s Pharmacy can be classified as common because they can be foreseen to happen if the right countermeasures are not taken. The identified problems are not special to this pharmacy alone; they also occur to any typical health facility (Trebble et al. 2010). However, this does not guarantee HMO’s Pharmacy to ignore them because if they are not attended to, they can gradually create special problems that can lead to detrimental effects such as patient death. With the technological changes within the health sector, the pharmacy need to replace its manual system with a digitalized medical prescription process to improve efficiency and accuracy, as well as, reducing the patient wait times (Griffin et al. 2016).
Main Statistical Tools to Use and Data
In developing a solution to this pharmacy, the initials aspect that needs to be considered is collecting data on issues such as medical errors and complaints reported along the phases of this model. With a keen evaluation of these complaints, it is easy to place each of them to the SIPOC elements presented in the model. Tools such as Excel, Stata, the R-program or the Statistical Package for the Social Sciences (SPSS) can assist in statistical analysis, especially in determining the association between the errors and the raised complains (Newell et al., 2011). If the results are statistically significant, then it implies that the pharmacy has to shift to a more advanced drug prescription system to reduce the wrong prescription incidences, hence enhancing patient satisfaction.
Proposed Solution and Assessment Strategy
With the above analysis, a key solution that can be recommended to the HMO’s Pharmacy is implementing an advanced digital prescription system which can be accessed by all the pharmacists, technician, and their respective assistants. Furthermore, the system needs to be accessible by the physicians through an intra or extranet platforms where the physicians can share information digitally without making any handwriting prescriptions that can be misleading – indicating that this will phase off the manual handwritten prescriptions hence increasing accuracy in terms of drug prescription and dosages (Bouchard et al. 2007). Still, it becomes efficient if counterchecking process can be done online even in the insurance verification process. Moreover, this would save more time for the patients and means that prescribed orders would be received in lesser period than the current waiting times experienced. Furthermore, it may become easier for patients to order medicines through mobile apps (like Consumer Value Stores apps) and organize for logistics.
Notably, to track the progress of this solution, the management can adopt statistical techniques such as Analysis of Variance (ANOVA) or T-tests to assess if there is any positive outcome of this program. Typically, a reduced number of complaints and errors is an indicator that the effectiveness of the newly proposed system which will be easily accessed through dependent sample T-tests. On the other hand, the effectiveness of the new system can also be assessed across several pharmacy branches or even when different categorical exploratory variables are considered such as pharmacist education levels among others. However, for the best medical outcome with the new system, continued training is recommended along with the usage of the new system. Training imparts the pharmacists with the knowledge to identify the potential issues that can compromise their decision making processes using the new prescription system and measures to avoid errors (Newell et al., 2011). It can be concluded that statistical thinking is a strong discipline that aids in sound and data-driven decision making for the best outcome. Particularly, in the health care setting, statistical thinking assists in investigating different problems that affect the service delivery process for better outcomes that improve patient safety and wellness.
References
Beardsley, R. S., Kimberlin, C. L., & Tindall, W. N. (2013). Communication skills in pharmacy practice: A practical guide for students and practitioners . Philadelphia, PA: Wolters Kluwer/Lippincott Williams & Wilkins.
Bennett, J. B., Lucas, G. M., Linde, B. D., Neeper, M. A., Hudson, M., & Gatchel, R. J. (2018). A process model of health consciousness: Its application to the prevention of workplace prescription drug misuse. Journal of Applied Biobehavioral Research , e12130.
Bouchand, F., Thomas, A., Zerhouni, L., Dauphin, A., & Conort, O. (2007). Pharmacists' interventions before and after prescription computerization in an internal medicine department. Presse medicale (Paris, France: 1983) , 36 (3 Pt 1), 410-418.
Griffin, P. M., Nembhard, H. B., DeFlitch, C., Bastian, N. D., Kang, H., & Mun ̃oz, D. A. (2016). Healthcare systems engineering . Hoboken, New Jersey: Wiley.
Newell, R., Burnard, P., & Newell, R. (2011). Research for evidence-based practice in healthcare . Chichester, West Sussex, U.K: Wiley-Blackwell.
Trebble, T. M., Hansi, N., Hydes, T., Smith, M. A., & Baker, M. (2010). Process mapping the patient journey through health care: an introduction. British Medical Journal 341 (7769), 394-397.
Tripathy, J. P., Bahuguna, P., & Prinja, S. (2018). Drug prescription behavior: A cross-sectional study in public health facilities in two states of North India. Perspectives in clinical research , 9 (2), 76.
Wei, L., & Shuqin, C. (2006). SIPOC model of the construction project integration and its organization support. Science Research Management , 27 (1), 138-144.
Weiss, M. C., & Sutton, J. (2009). The changing nature of prescribing: pharmacists as prescribers and challenges to medical dominance. Sociology of health & illness , 31 (3), 406-421.
