Due Saturday: APA Format With References Module 03 Wr 883837

Due Saturday Apa Format With Referencesmodule 03 Written Assignment

Compare and contrast how surveys can impact healthcare both negatively and positively, then explain what steps can be used in helping to eliminate biases in the healthcare industry as it relates to sampling and gender biases. Next, discuss the types of surveys that might be used for your course project topic – Electronic Medical Records (EMR) and how sampling and gender bias may color the information provided by surveys. This information will be submitted again in your module 6. Surveys are important for research and evaluation of data trends within patient populations.

The information they provide can lead to improvements in healthcare practice patterns through the development of treatment guidelines. Write a 3–5 page summary – This information will be submitted again in Module 6. Compare and contrast how surveys can impact healthcare both negatively and positively. Explain what steps can be used in helping to eliminate biases in the healthcare industry as it relates to sampling and gender biases. Regarding your course project - Electronic Medical Records (EMR): Discuss the types of surveys that might be used for your course project topic- Electronic Medical Records (EMR) Describe how your project - Electronic Medical Records (EMR) – may be affected by surveying bias

Paper For Above instruction

Surveys are a vital research tool in healthcare, providing insights into patient populations, healthcare practices, and outcomes. Their influence can be both beneficial and detrimental, depending on how they are designed, implemented, and interpreted. This paper compares and contrasts the positive and negative impacts of surveys on healthcare, explores strategies to mitigate biases such as sampling and gender biases, discusses relevant survey types for the course project on Electronic Medical Records (EMR), and examines how survey biases might affect this specific project.

Positive and Negative Impact of Surveys in Healthcare

Surveys have a significant positive impact on healthcare by enabling the collection of large-scale data that informs clinical practice, policy-making, and patient care improvements. They facilitate understanding of disease prevalence, treatment effectiveness, patient satisfaction, and health behaviors (Fowler, 2014). For instance, surveys can identify gaps in care delivery and help develop targeted interventions, thus improving treatment outcomes (Creswell & Creswell, 2018). Moreover, surveys can highlight disparities among different populations, prompting efforts toward equitable healthcare (Baker et al., 2018).

However, surveys can also exert negative effects if not properly designed or interpreted. Biased sampling or poorly worded questions may lead to inaccurate data, misinforming healthcare policies or interventions (Groves et al., 2009). For example, overrepresentation of certain demographics can skew results, leading to ineffective or inappropriate health strategies. Additionally, sensitive subjects might result in social desirability bias, where respondents provide socially acceptable answers rather than truthful responses (Dillman, Smyth & Christian, 2014).

Strategies to Eliminate Biases in Healthcare Surveys

Addressing biases is crucial for obtaining valid and reliable data. In terms of sampling bias, employing random sampling techniques ensures that every individual has an equal chance of selection, reducing the likelihood of systematic bias (Fowler, 2014). Stratified sampling can be used to ensure representation across different demographic groups, including gender, age, ethnicity, and socioeconomic status (Creswell & Creswell, 2018). To mitigate gender bias, researchers should design gender-neutral questions and actively include diverse populations in sample recruitment, ensuring that the data accurately reflects the varied experiences and health concerns of all genders (Baker et al., 2018).

Training survey administrators on cultural competence and neutrality can further reduce interviewer bias. Incorporating validated, standardized survey instruments helps ensure consistency and comparability of responses. Additionally, employing anonymity and confidentiality can encourage honest and uninfluenced answers from participants (Dillman et al., 2014). Combining qualitative and quantitative methods provides a comprehensive understanding and helps verify the accuracy of findings (Creswell & Creswell, 2018).

Survey Types for the Electronic Medical Records (EMR) Project

The course project on Electronic Medical Records (EMR) can utilize various survey formats to gather relevant data, such as customer satisfaction surveys, usability questionnaires, and clinician feedback forms. Customer satisfaction surveys can examine patient perceptions of EMR interfaces and their impact on care delivery (Hersh et al., 2015). Usability surveys evaluate how effectively healthcare providers interact with EMR systems, identifying potential issues that could compromise patient safety or efficiency (Bates et al., 2018). Clinician surveys can assess the impact of EMR on workflow, communication, and diagnostic accuracy (Menachemi & Collum, 2011).

It is essential to select survey types that are appropriate for specific research questions. For example, Likert scale questionnaires can quantify attitudes toward EMR usability, while open-ended questions can provide rich, descriptive feedback. Employing a combination of these survey formats allows for a comprehensive assessment of the system's strengths and shortcomings (Fowler, 2014).

Impact of Surveying Bias on EMR Projects

Survey bias can significantly influence the outcomes of the EMR project. For instance, if surveys predominantly attract responses from tech-savvy staff or certain demographic groups, results might not accurately reflect the experiences of all users (Menachemi & Collum, 2011). Gender bias, in particular, could manifest if male and female respondents experience different challenges or benefits with EMR systems, but their perspectives are unequally represented in the data (Baker et al., 2018). This skew could lead to biased conclusions about usability and the effectiveness of EMR implementations.

Mitigating such biases involves deliberate sampling strategies to ensure diverse participant inclusion and designing questions that are free from gendered language or assumptions. Additionally, providing multiple avenues for survey participation (e.g., online, paper-based, interviews) increases inclusivity and participation rates among varied demographic groups, thereby improving data accuracy and representing the full spectrum of user experiences (Dillman et al., 2014).

Conclusion

Surveys are powerful tools in healthcare research, capable of guiding improvements in patient outcomes and health system efficiencies. While they can be highly beneficial, careful attention to design and sampling methods is essential to avoid bias, ensure representativeness, and produce valid data. The application of appropriate survey types for the EMR project can yield valuable insights, but awareness of potential biases is vital for accurate interpretation. Ultimately, rigorous survey methodology enhances the reliability of data used to inform healthcare decisions and policy development.

References

  • Baker, R., Ward, E., & Smith, J. (2018). Addressing gender bias in healthcare surveys. Journal of Medical Ethics, 44(3), 124-132.
  • Bates, D. W., Cohen, M., Leape, L. L., et al. (2018). Reducing medical errors in electronic health records. Journal of Patient Safety, 14(2), 79-87.
  • Creswell, J. W., & Creswell, J. D. (2018). Research Design: Qualitative, Quantitative, and Mixed Methods Approaches. Sage Publications.
  • Dillman, D. A., Smyth, J. D., & Christian, L. M. (2014). Internet, Phone, Mail, and Mixed-Mode Surveys: The Tailored Design Method. John Wiley & Sons.
  • Fowler, F. J. (2014). Survey Research Methods. Sage Publications.
  • Groves, R. M., Fowler, F. J., Couper, M. P., et al. (2009). Survey Methodology. Wiley & Sons.
  • Hersh, W., Totten, A., Eden, K., et al. (2015). Data Quality in Electronic Health Records. Agency for Healthcare Research and Quality.
  • Menachemi, N., & Collum, T. H. (2011). Benefits and drawbacks of electronic health record systems. Risk Management and Healthcare Policy, 4, 47-55.
  • Schroeder, R., & McDonald, T. (2019). Understanding survey delivery modes and bias. Health Informatics Journal, 25(4), 1212-1222.
  • Wright, A., Sittig, D. F., Ash, J. S., et al. (2017). Current challenges and research opportunities in electronic health record usability. Journal of Biomedical Informatics, 66, 101-111.

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