- Time Management
- Medical Insurance
- Analysis Skills
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The New York University Health Evaluation and Analytics Lab (HEAL) was established in 2016 by the Department of Population Health at the NYU School of Medicine and the NYU Wagner School of Public Service. HEAL promotes applied research to help health sector organizations evaluate initiatives that seek to improve health outcomes among populations and to improve health care delivery practices. HEAL has built a unique New York State Medicaid claims file that captures information from every medical care claim paid by the New York State Medicaid program. This large data system allows for a wide range of evaluative work focused on improving care and outcomes for the most vulnerable New Yorkers, who rely on Medicaid to pay for their medical care needs. The data set also allows providers to better track the services received by patients they serve. Built in close collaboration with the New York State Department of Health, HEAL’s system allows for great flexibility in a) identifying individuals who are affected by new “treatments” (which could be new care approaches, new social policies, or new Medicaid policies); b) measuring outcomes for these individuals; and c) developing comparison samples to facilitate assessing impacts associated with the “treatments.” The data in its current iteration has information for the past 13 years and is updated quarterly.
New York University’s Health Evaluation and Analytics Lab is seeking an individual with strong programming, data management, and analytical skills to work on a team using New York State Medicaid claims data to evaluate health services and public health interventions being implemented at providers and public health agencies across New York State.
We are looking for a person with experience and graduate training related to health care delivery, public health and the financing of the United States health system. The ideal candidate will have had some experience working with health insurance claims data and/or electronic health records. While the position requires technical skills, we are particularly looking for a person to join our team who has a passion for making the health system work better for low income New Yorkers. The person hired would participate in designing applied research and evaluation studies that guide health system leaders in efforts to innovate and improve health care and public health delivery. Current projects include assessing the effectiveness of APPs to help patients manage their chronic conditions, projects that assess new models of managing care for high risk patients, and studies that look at the implications of new reimbursement approaches on the ability to provide effective care.
Responsibilities will include two types of work streams:
Downloading, merging, cleaning, documenting, and managing Medicaid claims datasets; analyzing data quality and integrity; identifying and solving data quality problems; coding and creating new variables; preparing analysis files; producing statistical summaries, charts and tables. The Medicaid files are complex but a stimulating challenge for a first rate coder.
Working on teams to design projects and to work with funders to make sure the analysis done is useful, accurate, and clearly communicated. We hire analysts with an interest and potential to help plan, initiate, and manage analytical projects helping health systems improve their efficiency and effectiveness. The person would be part of a growing, mission-driven research team interested in improving health care in New York.
Many positions that require similar skills exist within large health systems and within large consulting firms. This position at NYU promises candidates the opportunity to work on a broader set of issues than is typical and to learn a broader set of skills. Analysts have more flexibility in how they get work done and are encouraged to take on additional responsibilities as they progress in the job. There is an excellent set of peers to interact with and learn from. NYU offers a stimulating work setting where seminars, educational opportunities, and the chance to work with cutting edge thinkers is always present.
This position will be reporting to James Knickman and John Billings, who are on the faculty at both NYU Langone Medical School’s Department of Population Health and the NYU Wagner Graduate School of Public Service. Dr. Billings is a senior faculty member and former director of Wagner's Health Policy and Management Program. He is the principal investigator on numerous projects to assess the performance of the safety net for vulnerable populations and to understand the nature and extent of barriers to optimal health for low income populations. Dr. Knickman recently joined NYU's faculty to create an applied research initiative at NYU Langone Medical School and the Wagner School focused on evaluating innovations in the health sector using "Big Data." Previously, he was the President of the New York State Health Foundation and before that's was VP for Research and Evaluation at the Robert Wood Johnson Foundation. Tod Mijanovich is a research professor at the NYU Steinhart School who leads projects at HEAL. Dr. Mijanovich teaches advanced statistics courses and is an expert on coding, statistical analysis, and claims-based analytics. Sherry Glied, the dean of the Wagner School, also is an active senior researcher working on this initiative. She is a leading health economist who oversees projects focused on how population health interventions at the community level impact health outcomes and medical care utilization patterns. HEAL is funded by a range of foundations, federal, state and city government agencies, and a broad range of health care and social service organizations seeking help to answer important analytical questions related to health system improvement.
To qualify you must have a Masters degree in public health, health care administration, economics, or computer science. 2-5 years relevant experience. Attention to detail. Ability to manage complex research and analysis. Strong analytical, organizational and time management skills. Proficiency in SAS, coding tools such as Python and SQL, and other statistical programs. Effective verbal and written communication skills and interpersonal skills. Ability to interface effectively with all levels of management and must work and communicate effectively with both internal and external partners.
Qualified candidates must be able to effectively communicate with all levels of the organization.
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