Tag: nevin manimala

J Med Internet Res. 2024 Aug 9;26:e56413. doi: 10.2196/56413.

ABSTRACT

BACKGROUND: Patient complaints are a perennial challenge faced by health care institutions globally, requiring extensive time and effort from health care workers. Despite these efforts, patient dissatisfaction remains high. Recent studies on the use of large language models (LLMs) such as the GPT models developed by OpenAI in the health care sector have shown great promise, with the ability to provide more detailed and empathetic responses as compared to physicians. LLMs could potentially be used in responding to patient complaints to improve patient satisfaction and complaint response time.

OBJECTIVE: This study aims to evaluate the performance of LLMs in addressing patient complaints received by a tertiary health care institution, with the goal of enhancing patient satisfaction.

METHODS: Anonymized patient complaint emails and associated responses from the patient relations department were obtained. ChatGPT-4.0 (OpenAI, Inc) was provided with the same complaint email and tasked to generate a response. The complaints and the respective responses were uploaded onto a web-based questionnaire. Respondents were asked to rate both responses on a 10-point Likert scale for 4 items: appropriateness, completeness, empathy, and satisfaction. Participants were also asked to choose a preferred response at the end of each scenario.

RESULTS: There was a total of 188 respondents, of which 115 (61.2%) were health care workers. A majority of the respondents, including both health care and non-health care workers, preferred replies from ChatGPT (n=164, 87.2% to n=183, 97.3%). GPT-4.0 responses were rated higher in all 4 assessed items with all median scores of 8 (IQR 7-9) compared to human responses (appropriateness 5, IQR 3-7; empathy 4, IQR 3-6; quality 5, IQR 3-6; satisfaction 5, IQR 3-6; P<.001) and had higher average word counts as compared to human responses (238 vs 76 words). Regression analyses showed that a higher word count was a statistically significant predictor of higher score in all 4 items, with every 1-word increment resulting in an increase in scores of between 0.015 and 0.019 (all P<.001). However, on subgroup analysis by authorship, this only held true for responses written by patient relations department staff and not those generated by ChatGPT which received consistently high scores irrespective of response length.

CONCLUSIONS: This study provides significant evidence supporting the effectiveness of LLMs in resolution of patient complaints. ChatGPT demonstrated superiority in terms of response appropriateness, empathy, quality, and overall satisfaction when compared against actual human responses to patient complaints. Future research can be done to measure the degree of improvement that artificial intelligence generated responses can bring in terms of time savings, cost-effectiveness, patient satisfaction, and stress reduction for the health care system.

PMID:39121468 | DOI:10.2196/56413

J Med Internet Res. 2024 Aug 9;26:e58950. doi: 10.2196/58950.

ABSTRACT

BACKGROUND: Digital health research plays a vital role in advancing equitable health care. The diversity of research teams is thereby instrumental in capturing societal challenges, increasing productivity, and reducing bias in algorithms. Despite its importance, the gender distribution within digital health authorship remains largely unexplored.

OBJECTIVE: This study aimed to investigate the gender distribution among first and last authors in digital health research, thereby identifying predicting factors of female authorship.

METHODS: This bibliometric analysis examined the gender distribution across 59,980 publications from 1999 to 2023, spanning 42 digital health journals indexed in the Web of Science. To identify strategies ensuring equality in research, a detailed comparison of gender representation in JMIR journals was conducted within the field, as well as against a matched sample. Two-tailed Welch 2-sample t tests, Wilcoxon rank sum tests, and chi-square tests were used to assess differences. In addition, odds ratios were calculated to identify predictors of female authorship.

RESULTS: The analysis revealed that 37% of first authors and 30% of last authors in digital health were female. JMIR journals demonstrated a higher representation, with 49% of first authors and 38% of last authors being female, yielding odds ratios of 1.96 (95% CI 1.90-2.03; P<.001) and 1.78 (95% CI 1.71-1.84; P<.001), respectively. Since 2008, JMIR journals have consistently featured a greater proportion of female first authors than male counterparts. Other factors that predicted female authorship included having female authors in other relevant positions and gender discordance, given the higher rate of male last authors in the field.

CONCLUSIONS: There was an evident shift toward gender parity across publications in digital health, particularly from the publisher JMIR Publications. The specialized focus of its sister journals, equitable editorial policies, and transparency in the review process might contribute to these achievements. Further research is imperative to establish causality, enabling the replication of these successful strategies across other scientific fields to bridge the gender gap in digital health effectively.

PMID:39121467 | DOI:10.2196/58950

J Public Health Manag Pract. 2024 Aug 9. doi: 10.1097/PHH.0000000000001985. Online ahead of print.

ABSTRACT

OBJECTIVE: Maintaining a skilled public health workforce is essential but challenging given high turnover and that few staff hold a public health degree. Situating workforce development within existing structures leverages the strengths of different organizations and can build relationships to address public health challenges and health equity. We implemented and evaluated an innovative, sustainable model to deliver an established evidence-based public health (EBPH) training collaboratively among Prevention Research Centers (PRC), local and state health departments, and Public Health Training Centers (PHTC).

DESIGN: Quantitative data: quasi-experimental, 1-group pre-post. Qualitative data: cross-sectional. Data were collected between December 2021 and August 2022.

SETTING: Four US sites, each a partnership between a PRC, local or state health department, and a PHTC.

PARTICIPANTS: Governmental public health staff and representatives from other organizations that implement public health programs in practice settings.

MAIN OUTCOME MEASURES: Course participants completed a pre- and postcourse survey self-rating 14 skills on a 5-point Likert scale. Differences were analyzed using mixed effects linear models. In-depth interviews (n = 15) were conducted with course faculty and partners to understand: (1) resources contributed, (2) barriers and facilitators, (3) benefits and challenges, and (4) resources needed to sustain this model. Interviews were transcribed verbatim, and a thematic analysis identified themes.

RESULTS: Statistically significant increases in all skills were observed from pre- to postcourse (n = 241 at post, 90% response). The skills with the largest increases were understanding economic evaluation enough to inform decision-making (mean change = 1.22, standard error [SE] = 0.05) and developing an action plan (mean change = 1.07, SE = 0.07). Facilitators to delivering the course included having a shared goal of workforce development, existing course curricula, and dedicated funding for delivering the course.

CONCLUSIONS: Collaborative delivery of the EBPH training can ameliorate the effects of high staff turnover, strengthen academic-practice relationships, and promote population-wide health and health equity.

PMID:39121436 | DOI:10.1097/PHH.0000000000001985

Phys Rev Lett. 2024 Jul 26;133(4):047101. doi: 10.1103/PhysRevLett.133.047101.

ABSTRACT

Large deviation theory offers a powerful and general statistical framework to study the asymptotic dynamical properties of rare events. The application of the formalism to concrete experimental situations is, however, often restricted by finite statistics. Data might not suffice to reach the asymptotic regime or judge whether large deviation estimators converge at all. We here experimentally study the large deviation properties of the stochastic work and heat of a levitated nanoparticle subjected to nonequilibrium feedback control. This setting allows us to determine for each quantity the convergence domain of the large deviation estimators using a criterion that does not require the knowledge of the probability distribution. By extracting both the asymptotic exponential decay and the subexponential prefactors, we demonstrate that singular prefactors significantly restrict the convergence characteristics close to the singularity. Our results provide unique insight into the approach to the asymptotic large deviation limit and underscore the pivotal role of singular prefactors.

PMID:39121406 | DOI:10.1103/PhysRevLett.133.047101

Phys Rev Lett. 2024 Jul 26;133(4):046902. doi: 10.1103/PhysRevLett.133.046902.

ABSTRACT

We establish low-temperature resonant inelastic light scattering (RILS) spectroscopy as a tool to probe the formation of a series of moiré bands in twisted WSe_{2} bilayers by accessing collective inter-moiré-band excitations (IMBEs). We observe resonances in RILS spectra at energies in agreement with inter-moiré-band transitions obtained from an ab initio based continuum model. Transitions between the first and second moiré band for a twist angle of about 8° are reported and between the first and the third, and higher bands for a twist of about 3°. The signatures from IMBE for the latter highlight a strong departure from parabolic bands with flat minibands exhibiting very high density of states in accord with theory. These observations allow one to quantify the transition energies at the K point where the states relevant for correlation physics are hosted.

PMID:39121396 | DOI:10.1103/PhysRevLett.133.046902

Medicine (Baltimore). 2024 Aug 9;103(32):e39215. doi: 10.1097/MD.0000000000039215.

ABSTRACT

The relationship between workplace justice and nonfatal occupational accidents in a single-payer healthcare system has rarely been explored. As countries strive to achieve and sustain universal health coverage, healthcare workers’ occupational safety and health require greater concerns. We used the data from a national survey conducted on randomly sampled Taiwanese workers. One hundred forty eight males and 567 females, with a total of 715 healthcare workers aged 20 to 65, were analyzed. The workplace scale consisted of 4 subcomponents, including distributive justice, interpersonal justice, information justice, and procedural justice, and was dichotomized into low and high groups in each dimension. Logistic regression models examined the relationship between workplace justice and self-evaluated occupational accidents among healthcare employees. The prevalence of self-evaluated occupational accidents in healthcare employees was 15.54% and 11.64% for men and women, respectively. After adjusting variables such as sociodemographic variables, physical job demands, shift work status, work contract, and psychological job demands, regression analyses indicated that health employees with lower distributive justice, interpersonal justice, information justice, and procedural justice were significantly associated with self-evaluated occupational accidents both in males and females. Expanding the study to include healthcare systems in different countries could enhance the generalizability of the findings. Offering specific recommendations for policymakers and healthcare administrators to improve workplace justice and reduce occupational accidents.

PMID:39121330 | DOI:10.1097/MD.0000000000039215

Medicine (Baltimore). 2024 Aug 9;103(32):e38956. doi: 10.1097/MD.0000000000038956.

ABSTRACT

Social media has become a source of disseminating information to the public during the COVID-19 outbreak which has been a great advantage for healthcare centers. However, foreign anti-vaccination campaigns on social media increased the disbelief in vaccine safety. To our knowledge, the effects of social media on COVID-19 vaccine acceptance are not well-studied in low-income countries. The primary objective of this survey is to investigate Syrians’ dependence on social media platforms to get information about vaccines, and to what extent it affects their vaccination decision. A web-based cross-sectional study was conducted in Syria from May 26th to July 26th, 2022 using an online questionnaire as Google Form posted on various social media platforms. The questionnaire consisted of 53 questions related to each of the socio-demographic characteristics, beliefs, and knowledge about COVID-19 vaccination, willingness to get vaccinated, and social media frequency use and its effects. Univariate and multivariate logistic regression was performed to identify factors associated with vaccination behavior. A total of 780 questionnaires were completed; around 42.2% of study participants did not get the vaccine, and 24% would take it only under compulsory rules. Also, only 3.08% of the participants answered correctly on the knowledge-evaluation questions. Results of the univariate analysis showed that being female, living in an urban residential area, and having good vaccine knowledge were positive predictors of vaccine receiving. The unvaccinated group had a higher likelihood of being college students, not trusting in the vaccine, knowing relatively less about the vaccine, and not having previously been exposed to the virus. No significant correlation between vaccination status and using social media was shown in our investigation. However, our results show the importance of social media information in health-related decisions in war-torn countries and emphasize further investigations to confirm causality and determine the best health policy choice.

PMID:39121327 | DOI:10.1097/MD.0000000000038956

Medicine (Baltimore). 2024 Aug 9;103(32):e39222. doi: 10.1097/MD.0000000000039222.

ABSTRACT

We aimed to determine the association between frailty and cognitive status of the elderly population in Korea. We examined data from 9920 elders who participated in the 2020 Survey of Living Conditions and Welfare Needs of Korean Older Persons. Frailty was assessed using the Korean version of the Fatigue, Resistance, Ambulation, Illnesses, and Weight Loss scale. The Korean mini-mental status examination was used to test cognitive function. Several logistic regression analysis was performed, with correction for several confounding variables (socioeconomic, health behavior, psychological characteristics, and functional status), to evaluate the relationship between frailty and cognitive state. Of the elderly population in Korea, 1451 (14.6%) were frail and 5977 (60.3%) were pre-frail. Compared to the non-frail group (20.3%), cognitive impairment was considerably higher in the pre-frail (33.1%) and frail (39.8%) groups. When compared to the non-frail group, cognitive impairment was substantially linked to a higher risk of frailty after adjustment (pre-frail odds ratio [OR]: 1.66, 95% confidence interval [CI]: 1.47-1.88; frail OR: 2.00, 95% CI: 1.68-2.37). When cognitive impairment and frailty subcomponents were present, there was a higher likelihood of severe resistance (OR: 1.89; 95% CI: 1.70-2.11) and ambulation (OR: 1.46, 95% CI: 1.32-1.63) issues. Frailty is associated with cognitive impairment.

PMID:39121321 | DOI:10.1097/MD.0000000000039222

Medicine (Baltimore). 2024 Aug 9;103(32):e39055. doi: 10.1097/MD.0000000000039055.

ABSTRACT

Polycystic ovary syndrome (PCOS) is a disease caused by excessive ovarian androgen secretion due to hypothalamic-pituitary-ovarian hormone abnormalities. We retrospectively investigated the treatment status of patients diagnosed with PCOS who visited a domestic tertiary hospital in order to analyze the use patterns and safety of drugs. Patients diagnosed with PCOS between July 2014 and September 2022 were examined, excluding patients younger than 13 years and those not receiving medication. Patients aged 21 years or younger were designated as the adolescent group and patients aged 22 years or older were designated as the adult group for comparative statistical analysis. The total number of patients was 212, including 105 adolescents (49.5%) and 107 adults (50.5%). Comorbidities were ovarian cyst in 20 (9.4%) patients, endometriosis in 19 (9%), diabetes in 14 (6.6%), thyroid dysfunction in 12 (5.7%), hypertension in 10 (4.7%), dyslipidemia in 10 (4.7%), and androgenic alopecia in 6 (2.8%). Symptoms were oligomenorrhea in 91 (42.9%) patients, amenorrhea in 72 (34%), hirsutism in 36 (17%), acne in 24 (11.3%), and infertility in 10 (4.7%). During the study period, 114 patients (53.8%) were prescribed medroxyprogesterone acetate (MPA), 66 (31.1%) were given oral contraceptives (specifically, ethinyl estradiol + drospirenone prescribed to 52 (24.5%)), and 17 (8%) were concurrently prescribed MPA and oral contraceptives. Forty-five (21.2%) patients changed prescriptions, with 10 (22.2%) switching due to side effects and 8 (17.8%) due to a therapeutic failure. A total of 5 patients (2.4%) discontinued the drug. Adverse drug reactions occurred in 15 patients (7.1%), with 5 being adolescents (4.8%) and 10 being adults (9.3%). MPA alone and ethinyl estradiol with drospirenone were the most prescribed medications for PCOS. Over the study, 45 patients changed prescriptions, 50 were lost to follow-up, and 5 adults discontinued medications.

PMID:39121320 | DOI:10.1097/MD.0000000000039055

Alzheimers Dement. 2023 Dec;19 Suppl 24:e082773. doi: 10.1002/alz.082773.

ABSTRACT

BACKGROUND: This study aims to detect direct cost savings with early detection of Alzheimer’s disease (AD) and related dementias. We hypothesize that earlier diagnosis of AD can reduce direct healthcare costs and unnecessary utilization. A retrospective case-control study is conducted using Optum Market Clarity Claims/EHR linked dataset on Medicare Advantage population from Jan 1 2011 to Dec 31 2020.

METHOD: Based on whether the patient was diagnosed with prior Mild Cognitive Impairment (MCI) before progressing to an AD diagnosis per ICD-9/10 cm diagnosis codes, AD patients are categorized into early vs. late diagnosis (for those who were identified in the MCI stage vs not). Both groups are compared on annual all-cause healthcare costs and frequency of encounters longitudinally spanning across 6 years pre-index period and 3 years post-index with AD diagnosis date as the index date. To understand the impact of early diagnosis, we estimated total cost, medical service cost and drug cost post-index within the first 3 years by risk adjustment on baseline demographics, comorbidities, procedures, lab tests, and visits prior to AD diagnosis using propensity score weighting.

RESULT: Early-diagnosed patients (N = 421) have a lower utilization rate of inpatient skilled nursing facility, nursing home and hospitalization than late-diagnosed patients (N = 2817) after being diagnosed with AD. We also observed average medical service cost savings in all 3 years post-index, ranging from $320 to $2556 per patient per year. Per cost allocation analysis, medical service cost savings are observed in inpatient, emergency room, inpatient SNF, urgent care, nursing home/assisted living, and observation unit. Risk adjusted cost savings with statistical significance are observed in year 3 post-index. The mean cost difference between late and early diagnoses: total cost ($15472.11), medical service ($15013.7) and medication cost ($1601.61).

CONCLUSION: The study highlights opportunities for earlier diagnosis and suggests long-term healthcare savings for AD patients who have been diagnosed at MCI stage compared to patients who were directly diagnosed with AD. Further research is needed to drill down into the driver of cost savings and whether it is attributable to disease management of MCI or other complications.

PMID:39120933 | DOI:10.1002/alz.082773