Lessons Learned During COVID-19 Pandemic - A Case Study in a Las Vegas Health Clinic
Bibliographic record
Abstract
Lessons Learned During COVID-19 Pandemic - A Case Study in a Las Vegas Health Clinic C J Howell-Canada,MD,FAAP, L A Canada, MD Abstract Background In the wake of the COVID-19 pandemic, health care facilitypreparedness is on the forefront of hospital administrators decision making Telehealth rapidly became themost important function of healthcare facilities to effectively manage and serve patients via telemedicine andface-to-face video chat appointments Maintaining the ability to continue to provide modified well child checksthrough telemedicine is important for pediatric providers to ensure the continuity of care at all age levels Telehealth gives the opportunity to address acute care concerns for the pediatric population Methods In thispaper, we analyze the foundation necessary to implement and support telemedicine/telehealth servicesduring a pandemic in a union sponsored medium-sized health care center in Las Vegas, NV This paperanalyzes and outlines the healthcare process of determining ways to ensure episode preparedness andeffective communication from the organizational level Data was collected through retrospective observation and detailed accounts from organizational information distribution and provider input Results The lessonslearned are that health care providers need an informed decision making process;communication flow fromhealth care administration is a priority, needing to be disseminated without barriers;all possibilities need tobe exhausted to avoid disruption of day-to-day medical services;medical policies and procedures need to beestablished to encompass all aspects of medical services provided (in-person, via telephone, telemedicine);and operational decisions should reflect effective communication along with preparedness with transparency Keywords: telehealth, telemedicine, organizational preparedness, healthcare process
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.017 | 0.005 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".