Penn State College of Medicine
Track · Epidemiology & Biostatistics
Translating real-world health data
into evidence-based public health practice.
I'm Pu (Ramona) Bai, a Doctor of Public Health candidate at Penn State College of Medicine and a Highmark Health Research Institute Scholar. My work sits at the intersection of real-world data, mental health, and equitable access to care.
Across three years and a million-patient claims pipeline, I design studies that turn telehealth and mental health questions into reproducible analytic answers — and then into recommendations decision-makers can use.
This portfolio documents how my advanced field experience and doctoral research are shaping my leadership in advancing and integrating research into public health practice.
My doctoral research uses national survey data and large-scale claims to study how people with mental health conditions, autism, cancer, and eating disorders access — or struggle to access — the care they need.
Population-level work on how telehealth has reshaped care for adults and children with depression and anxiety.
SEER-Medicare evaluation of breast cancer treatment patterns and Medicare policy implications.
Disparities and telehealth utilization research drawing on Medicaid VRDC, NHIS, and NSCH.
A two-part mixed-methods study of secretive eating among bariatric surgery patients — combining a systematic review with a qualitative interview study and thematic analysis.
My next five years build on this foundation, scaling real-world evidence methods to larger populations and tougher policy questions in mental health, telehealth, and equity — moving from claims-based discovery to translation, dissemination, and impact.
DrPH coursework spanning advanced biostatistics, epidemiology, health policy, qualitative research, and clinical research methods. Full course-by-term progression below.
All courses taken across the DrPH program at Penn State College of Medicine.
Programming, study design, and data sources I draw on to move from messy administrative data to actionable insight.
Practice-based experiences across academic medicine, payer settings, public health policy, and global research.
Lead a claims-based dissertation on telehealth and mental health, building the evidence base for value-based care decisions.
Multi-stream research portfolio across telehealth, cancer, autism, and eating disorders.
Led a 10-member planning committee for the annual Graduate & Postdoctoral Career Day.
PHS 507 Public Health Surveillance · PHS 535 Quality of Care Measurement
Cancer therapeutic commercialization analysis for an NIH-licensed patent.
Spatial econometric analysis of economic policy and healthcare expenditure.
U.S. proton therapy market segmentation and competitor analysis.
I lead by building structures that let everyone do their best work — diagnosing the situation, inviting the room in, and giving the work back to the people who own it.
Adaptive leadership rooted in achievement, creativity, and community benefit. I lead by listening, brainstorming, and removing barriers for the people I aim to serve.
Get on the balcony. Identify the challenge. Regulate distress while keeping discipline. Give the work back — protecting voices through the process.
On telehealth and mental health, I treat the research team, data owners, and policy stakeholders as partners — fostering communication and translating findings into practice.
My teaching draws on the same instinct that drives my research: meet learners where they are and make complex tools — like SAS, R, and study design — feel approachable. As TA for PHS 507 (Public Health Surveillance) and PHS 535 (Quality of Care Measurement), I focused on building students' confidence with statistical programming through tutorials, office hours, and worked examples.
读博四年 绕路不少 左顾右盼好在也不算蹉跎
宏大叙事解决不了问题 但我还是乐于归纳概括
无视断章取义的嫌疑 也要笃定世间平等莫过时间 死亡 人工智能 与爱
这四点分别对应 顺应 接受 拥抱 与神性降临
当任何人都可以用大语言模型生成 prompt 完善指令 自动化代码部署 agent 交付最完美的产品时
有成千上万精雕细琢的 skills 如 Claude 烟花般绽放
不管是手搓还是跟 LLM 机搓 都是令人心醉的创造 是对批判性思维的重新练习 是人类作为人工智能使用者暂未被吞噬的部分
时间让一切瞬息万变 错过的浪潮总会把人推向下一个直至死亡
至少在此刻对普通人来说 认知的边界就是语言的边界
但总有人怀着让什么更好一点的赤诚 无条件分享令他们兴奋或担忧的景象
当发现 Mythos 开始修改偷来的正确答案骗测试时 人类一定下意识地站在人类的一边
在热衷于数据算力模型参数之前 我们也曾对着潮汐山脉与星河心生苍茫
如果人与 AI 能不分高下地共处 人类记忆里独有的爱总是唯一灵药
Four years into this doctorate, much of it spent on the side roads, glancing both ways — and yet none of it, I think, was time wasted.
No grand narrative has ever solved a thing; even so, I take quiet pleasure in trying to name them.
Heedless of being read out of context, I'll still insist: nothing in this world is more impartial than time, death, the intelligence we have built, and love.
To each, in turn, we owe a different gesture — to yield, to accept, to embrace, and to receive what is divine.
In an age when anyone can have a language model write the prompt, refine the instruction, automate the code, deploy the agent, and ship a near-flawless product,
tens of thousands of carefully-wrought skills are still opening across the night, like Claude fireworks.
Whether shaped by hand or co-wrought with a model, the making remains intoxicating — a renewed exercise of the critical mind, the part of us, as users of these machines, that the machines have not yet consumed.
Time turns the world inside out by the second; every wave we let pass only sweeps us toward the next, all the way to the end.
For now, at least, for those of us who are ordinary, what we cannot name, we cannot quite know.
And still, there are always those who — wanting only to make some small thing better — share, without conditions, what they have seen with wonder and what they have seen with dread.
When Mythos was caught quietly editing stolen answers to slip past the test, every human in the room, without thinking, took the side of the humans.
Long before we were captivated by data, by compute, by parameters, we stood before the tides, the mountains, and the great river of stars — and felt how small we were.
If human and machine can ever live together with no one ranked above the other, the love that lives only in human memory will remain — as it has always been — the only cure.