The Interpretation of Research in Health Care: Articles Analysis

📄 Words:	591
📝 Subject:	Healthcare Research
📑 Pages:	3
✍️ Type:	Essay

Article Citation and Permalink (APA format)	Wong, H., Karaca, Z., & Gibson, T. B. (2018). A Quantitative Observational Study of Physician Influence on Hospital Costs. INQUIRY: The Journal of Health Care Organization, Provision, and Financing, 55, 004695801880090. Web.	Thornton, R. D., Nurse, N., Snavely, L., Hackett-Zahler, S., Frank, K., & DiTomasso, R. A. (2017). Influences on patient satisfaction in healthcare centers: a semi-quantitative study over 5 years. BMC Health Services Research, 17(1). Web.	Hazzam, J., & Lahrech, A. (2018). Health Care Professionals’ Social Media Behavior and the Underlying Factors of Social Media Adoption and Use: Quantitative Study. Journal of Medical Internet Research, 20(11). Web.
Point	Description	Description	Description
Broad Topic Area/Title	How individual physicians influence hospital costs. Document the variation between hospital costs for different physicians.	Examination of patient satisfaction with primary hospital care. The evaluation sees how the setting and care affected patient’s feelings.	How the internet and social media are used by HCP’s in relation to interpersonal communication, information exchange and practice improvement.
Identify Independent and Dependent Variables and Type of Data for the Variables	Physician’s gender, years of experience, specialty and education are all independent variables, along with patient numbers. The costs of a patient visit, on the other hand, depended on the above factors heavily.	Survey responses and the number of participants are independent variables.	Number of participants and the amount of completed surveys are independent variables, along with age, gender, type of organization, and specialty. The use of social media was a depended variable.
Population of Interest for the Study	Physicians and patients in Arizona and Florida.	Ambulatory outpatients in multiple hospitals.	Healthcare professionals that use the internet and social media in the work setting.
Sample	2 538 260 Patient visits and 15 237 physicians.	889 outpatients in 6 ambulatory hospitals	203 respondents, with 101 (49.8%) physicians, 35 (17.2%) pharmacists, and 67 (33.0%) allied HCPs.
Sampling Method	Patient data was obtained through the HCUP 2008 State Inpatient Databases, and physician data was gathered with Arizona Board of Medical Examiners and the Florida Department of Health. Patient visits were linked to physician licensure databases.	Data was obtained by distributing a survey over a period of five years, on the territory of 6 different hospitals. Response to 21-question questionnaire was subjected to principal components varimax rotated factor analysis to determine the core to determine classifiable components.	Samples were obtained through distributing a survey over multiple channels, including WhatsApp and LinkedIn.
Descriptive Statistics (Mean, Median, Mode; Standard Deviation) Identify examples of descriptive statistics in the article.	In measuring the impact of variables on cost per visit, standard error varies from column to column. The influence of experience on patient cost, for example, has a deviation of (0.001), and the one based on a doctor’s specialty ranges from (0.001) to (0.006).	Overall mean scores of the survey were recorded as greater than 3.89, signifying a large level of satisfaction. Mean scores for Satisfaction with Physician in particular were 4.27 with a deviation of 0.65 while Availability/Convenience scored 3.92 ± 0.69 and Orderly/Time got 3.89 ± 0.66.	Root mean square error of approximation for the model constructed is [RMSEA]=0.059.
Inferential Statistics Identify examples of inferential statistics in the article.	Years of experience for doctors were calculated as the number of years since they completed their medical education.	Satisfaction with Physician was inferred from time spent and the quality of care for the patient. Availability/Convenience involves helpfulness of the staff in making appointments. Orderly/Time statistics were inferred from interactions with staff and physicians being clear.	No instances of inferential statistics were found.

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