Compilation of extensive patient data is crucial for understanding the broader context of health, as it encompasses a diverse array of chronic disease comorbidities, detailed assessments of living conditions, as well as insights into educational background. This comprehensive data collection serves as a foundational element for personalized healthcare initiatives.
Utilize the Pittsburgh Sleep Quality Index (PSQI)5 score for assessing the sleep quality of patients: the PSQI score serves as a vital tool in evaluating participants’ sleep quality. It includes a total of 19 self-assessment items and 5 additional assessment items, with the 19th self-assessment and the 5 extra items classified as non-scored. The structured PSQI questionnaire assesses sleep through 18 scored items, which are organized into seven distinct components: sleep quality, sleep onset latency, sleep duration, habitual sleep efficiency, sleep disturbances, use of sleeping medication, and daytime dysfunction. Each component is rated on a scale ranging from 0 to 3, and the aggregate of these ratings produces a total PSQI score, which ranges from 0 to 21 points; higher scores are indicative of poorer sleep quality. The PSQI scale has demonstrated robust reliability, with homogeneity reliability scores ranging from 0.537 to 0.846, a Cronbach’s alpha coefficient of 0.833, and a split half reliability of 0.853. The seven key components together contribute a cumulative rate of 58.42%, and their loadings fall between 0.475 and 0.868.
Utilize the Food Frequency Questionnaire (FFQ)6 for evaluating the dietary quality of patients: this specialized questionnaire is meticulously designed to capture the frequency of specific food consumption over predetermined durations—be it one day, one week, or one month. Its primary objective is to explore the dietary patterns prevalent within the population for research and health improvement purposes. While it offers valuable insights into dietary habits and trends, it does not provide a comprehensive evaluation of overall dietary intake. The FFQ emphasizes individual dietary habits, thereby delivering data on past nutrient and food consumption. The relevant validity coefficients for the FFQ range from 0.4 to 0.7 and 0.3 to 0.6, showcasing a commendable degree of accuracy in dietary assessment.
Utilize the Perceived Social Support Scale (PSSS)7 for the collection of social support data: this scale serves as an important assessment tool for evaluating an individual’s subjective understanding and perception of social support. Comprising 12 thoughtfully crafted items, the scale employs a 7-point scoring system to measure perceived support from various sources, including family and friends. The cumulative score derived from these items reflects the individual’s overall perceived level of social support. With its adaptation for local application, the PSSS has proven effective among Chinese cancer patients. The Cronbach’s alpha coefficients for the total scale and its three subscales read as follows: 0.923, 0.909, 0.866, and 0.789 respectively. Furthermore, the half fold reliability of the PSSS ranges from 0.801 to 0.915, signifying excellent reliability.
Utilize the Short Form-36 (SF-36)8 for evaluating the patients’ quality of life: this widely respected scale was derived from the Boston Health Study in the United States and is rooted in the Medical Outcome Study Scale developed by Stewart in 1988. The Chinese version of the SF-36, translated in 1991 by the Social Medicine Teaching and Research Office at Zhejiang University School of Medicine, includes eight domains: physical functioning, role limitations due to physical health, bodily pain, general health perceptions, vitality, social functioning, role limitations due to emotional problems, and mental health. The test-retest reliability for the SF-36 across various domains is as follows: physiological function (PF) at 0.78, role limitations due to physical health (RP) at 0.85, physical pain (BP) at 0.92, overall health evaluation (GH) at 0.82, vitality (VT) at 0.77, social function (SF) at 0.71, role limitations due to emotional problems (RE) at 0.79, and mental health (MH) at 0.66. The Cronbach’s alpha coefficients for each domain are equally impressive: PF at 0.89, RP at 0.75, BP at 0.84, GH at 0.86, VT at 0.78, SF at 0.72, RE at 0.86, and MH at 0.50.
Assessment of living environment: conducting a ward environment safety risk assessment requires a systematic approach encompassing several key elements. Initially, identifying potential risk factors includes thorough observation and analysis of the ward environment to uncover issues such as improper equipment placement, electrical hazards, and inadequate protective facilities. Following the identification phase, assessing the severity of these risks necessitates evaluating each potential factor based on its likelihood and impact, thereby gauging its effect on both patients and healthcare workers. A critical part of this assessment involves evaluating the effectiveness of existing control measures to determine whether they adequately mitigate the likelihood and severity of risks present. Developing improvement plans derives from these evaluations, resulting in clear delineation of actions needed and improvement objectives, which aim to enhance equipment configuration, environmental safety, and staff training. Implementing the proposed improvement measures follows, requiring adherence to control strategies outlined in the improvement plan, including necessary upgrades and employee training. Continuous monitoring and evaluation of improvement effectiveness is crucial, necessitating regular assessments to refine the plans iteratively and ensure ongoing enhancements.
Assess the TSH level in the patient’s peripheral blood: in this process, the patient is required to fast overnight before being awakened in the morning for blood collection, during which 5 ml of venous blood is drawn. The concentration of TSH in the patient’s peripheral blood is determined through a standard chemiluminescence immunoassay, with reagents sourced from Shanghai Biyun Tian Company. It is imperative that blood samples are collected around the same time point each day (morning fasting) to ensure consistency. Venous blood collection techniques are followed, utilizing sterile blood collection tubes that are either with or without anticoagulants, contingent upon subsequent processing and analysis requirements. Each sample typically requires 1 to 2 mL of blood. Following collection, the blood sample is centrifuged at 4 °C (for instance, 3000 g for 10 minutes) to separate plasma or serum. The separated plasma or serum is then transferred to a new sterile tube and appropriately labeled. For preservation, the resulting plasma or serum should be stored at -80 °C to maintain its integrity for future analyses.
Statistical analysis
All data analyzed in this study were processed utilizing SPSS28.0 statistical analysis software (IBM, USA). The measurement data are presented as “mean ± standard deviation” (± s), with independent sample t-tests employed for intergroup comparisons. Count data are expressed as percentages (%), and intergroup differences are analyzed using chi-square analysis. To explore the correlation between various variables and symptoms of anxiety and depression, the Pearson correlation coefficient analysis was applied. This method quantifies the linear relationship between anxiety and depression symptoms and a range of influencing factors. To further investigate the influencing factors associated with anxiety and depression in patients, multiple logistic regression analyses were conducted. A significance level of P
**Interview with Dr. Linda Chang: Advancements in Patient Data Collection for Personalized Healthcare**
**Editor:** Thank you for joining us today, Dr. Chang. Your work focuses on the importance of compiling extensive patient data for personalized healthcare initiatives. Can you explain why this data is so crucial in understanding patients’ health?
**Dr. Chang:** Thank you for having me. Comprehensive patient data is essential because it provides a holistic view of an individual’s health context. Chronic diseases often come with various comorbidities, and understanding factors like living conditions and educational background can significantly influence treatment outcomes. This extensive dataset allows us to personalize healthcare strategies that truly address patients’ unique needs.
**Editor:** That makes a lot of sense. Can you tell us about some specific tools you utilize in your assessments, starting with the Pittsburgh Sleep Quality Index (PSQI)?
**Dr. Chang:** Absolutely! The PSQI is a fundamental tool for evaluating sleep quality. It consists of 19 self-assessment items that assess sleep on multiple dimensions, such as sleep disturbances and daytime dysfunction. The scoring ranges from 0 to 21; a higher score indicates poorer sleep quality. Reliable scoring ensures that our assessments can guide sleep intervention strategies effectively.
**Editor:** I understand dietary habits are another critical component. How does the Food Frequency Questionnaire (FFQ) fit into your assessments?
**Dr. Chang:** The FFQ is designed to gauge the frequency and types of food consumption among patients over specific periods. While it doesn’t provide an overall dietary intake assessment, it reveals patterns and trends in dietary habits, which can influence both physical and mental health. The validity coefficients indicate that it is a reliable source for this type of data.
**Editor:** Moving on to social factors, how do you evaluate social support among patients, and what tool do you use for that?
**Dr. Chang:** We use the Perceived Social Support Scale (PSSS) to assess an individual’s perception of social support from their circle, including family and friends. It consists of 12 items evaluated on a 7-point scale. The high reliability scores validate its effectiveness, particularly in populations like Chinese cancer patients where social support can significantly impact recovery and treatment adherence.
**Editor:** Quality of life is another crucial aspect of patient assessment. How do you measure it?
**Dr. Chang:** For quality of life assessments, we utilize the Short Form-36 (SF-36). This scale covers eight domains including physical functioning and mental health, providing a comprehensive overview of a patient’s quality of life. The Cronbach’s alpha coefficients demonstrate the consistency of the scale, which is vital for accurate assessments.
**Editor:** Safety in the healthcare environment is as vital as patient health. Can you walk us through how you assess living environments in healthcare settings?
**Dr. Chang:** Certainly! Conducting a ward environment safety risk assessment involves identifying potential risks, evaluating their severity, and analyzing the effectiveness of existing safety measures. This systematic approach allows us to develop targeted improvement plans, implement necessary changes, and continue monitoring safety practices to protect both patients and healthcare workers.
**Editor:** Lastly, you mentioned assessing TSH levels in patients. Can you elaborate on the process and its significance?
**Dr. Chang:** Yes, assessing TSH levels helps us evaluate thyroid function, which can impact various metabolic processes and overall health. The process involves overnight fasting, followed by the collection of venous blood for analysis. This endocrine assessment is crucial for diagnosing conditions related to thyroid dysfunction.
**Editor:** Thank you, Dr. Chang, for this insightful discussion on the importance of comprehensive patient data collection in personalized healthcare. Your work is paving the way for improved patient outcomes!
**Dr. Chang:** Thank you for having me! It’s vital we continue to invest in better data collection methods to enhance patient care.
