MDP Research Projects

The MDP is committed to offering students novel and creative design opportunities exploring the diverse, multidisciplinary fields of energy, environment, healthcare, and culture. Student design teams will be fully immersed in the research laboratory, collaborating with their faculty co-mentors, and using state-of-the-art equipment. These projects will fully engage the students and provide them the opportunity to see how multidisciplinary collaboration can lead to innovative results.

The following faculty-mentored design projects are available during the 2019-2020 MDP. Select a link for an overview of the project, associated faculty co-mentors, project prerequisites, and related publications.

MDP Design Projects

    1) Conformal Coating of Curved Surfaces using a Novel Multi-Nozzle Bio-Imprinter  

    2) DNA Polymerase at the Single-Molecule Level 

    3) Fate of Antibiotic Resistant Genes in Human Sewage 

    4) Multidisciplinary Applications of Machine Learning towards Identifying Social Conventions 

    5) Optical Measurement System for Characterizing Vaping Smoke 

    6) Predictors of Patient Experience in the Pediatric Setting  




 Project #1:  Conformal Coating of Curved Surfaces using a Novel Multi-Nozzle Bio-Imprinter
Faculty Mentors:  
Mr. Lawrence KulinskyMechanical & Aerospace Engineering

Professor Arash KheradvarBiomedical Engineering

Description:  The goal of the following research project is to design a new form of 3D printing that will allow for conformal coating. Conformal coating refers to technology that will allow a 3D printer to coat a contour of irregular surfaces. By conforming 3D printing technology to coat by ejecting gel deposits along an irregular surface, new methods of bioengineering become possible. Using this technology, this project aims to create a bio-imprinter, capable of fully coating an existing 3D heart valve with hydrogel stem cells as its ink.

Students Involvement and Expected Outcomes:

Currently, the project is being researched under two graduate students; Ramses Torres and Simon Tran. The inclusion of undergraduate researchers will form a mentor and mentee apprenticeship, in which the undergraduate student may shadow the graduate student in experimentation methods and analysis.

Roles for the team to complete each phase however can be subdivided into multiple tasks:

Experimentation Engineer:
- Experimentation Engineer would be responsible for setting up experiments and finding new experimental methods for analysis. Research articles and compile data to help towards innovations or improvements in the project will also entail in responsibilities. Organize material and design procedures for the following engineers to follow.

Design Engineer:
- The Design Engineer would be responsible for creating the Solidworks model for all experimentation contraptions. Creating, fabricating the experimentation design and coordinating with the experimentation engineer to develop all models needed for testing.

Manufacture Engineer:
- The Software Engineer would be responsible for coding CNC code in G-code. The primary role of the software engineer would be to control the CNC aspect of the printer. Other responsibilities include electrical wiring and coding of stepper motors used in the printer and pump system.

All engineers on the project will also work towards cell culture training for Phase 2 cell viability test runs. All engineers will also assist in lab report write-ups, design binder updates and attendance of meetings with all possible collaborators and future sponsors.

Prerequisites: 
• Matlab experience (MAE 10) (optional)

• Machining and lathing experience and sticker for Machine shop (optional)

• Fabrication (woodshop or Fabworks equivalent) (optional)

• 3D printing experience (optional)

• Solidworks Experience (MAE 52)

• Arduino experience (MAE 106/equivalent course)

• CNC General Knowledge (use, management, setup, etc).

• G-Code Knowledge (Standard understanding of G-Code, coding, procedures, etc).

• Basic electronics (soldering, circuit design, circuit troubleshooting, etc).

• Basic electrical knowledge (AC/DC current use and management, current and voltage adjustments for various electroncial devices, etc).

• Basic Fluid Dynamics (fluid flow properties and types, basic calculations (pressure, mass flow, Reynolds Number, etc), ability to build devices according to the fluid dynamics characteristics).

• Basic Heat Transfer (heat capacity, temperature dependent properties, temperature control, etc).

• Basic Mechanical Stress (stress types and properties, stress calculations, etc).

Recommended Web sites and publications: 
   Negro, Andrea, et al. “3D Inkjet Printing of Complex, Cell-Laden Hydrogel Structures.” Nature News, Nature Publishing Group, 20 Nov. 2018, www.nature.com/articles/s41598-018-35504-2.: www.nature.com/articles/s41598-018-35504-2.
   Bausch, Nils, et al. “3D Printing onto Unknown Uneven Surfaces.” IFAC-PapersOnLine, Elsevier, 10 Nov. 2016, www.sciencedirect.com/science/article/pii/S2405896316322765.: www.sciencedirect.com/science/article/pii/S2405896316322765
   Chang, Robert, et al. “Effects of Dispensing Pressure and Nozzle Diameter on Cell Survival from Solid Freeform Fabrication-Based Direct Cell Writing.” Tissue Engineering. Part A, U.S. National Library of Medicine, Jan. 2008, www.ncbi.nlm.nih.gov/pubmed/18333803.: www.ncbi.nlm.nih.gov/pubmed/18333803
   Park, Hyunchul, et al. “Multiscale Transfer Printing into Recessed Microwells and on Curved Surfaces via Hierarchical Perfluoropolyether Stamps.” Small (Weinheim an Der Bergstrasse, Germany), U.S. National Library of Medicine, 15 Jan. 2014, www.ncbi.nlm.nih.gov/pubmed/23606663.: www.ncbi.nlm.nih.gov/pubmed/23606663
   Chen, Zhichao, et al. “3D Multi-Nozzle System with Dual Drives Highly Potential for 3D Complex Scaffolds with Multi-Biomaterials.” SpringerLink, Korean Society for Precision Engineering, 9 May 2017, link.springer.com/article/10.1007/s12541-017-0090-8.: link.springer.com/article/10.1007/s12541-017-0090-8
   Attalla, Rana, et al. “3D Bioprinting of Heterogeneous Bi- and Tri-Layered Hollow Channels within Gel Scaffolds Using Scalable Multi-Axial Microfluidic Extrusion Nozzle.” Biofabrication, U.S. National Library of Medicine, 27 Dec. 2018, www.ncbi.nlm.nih.gov/pubmed/30537688.: www.ncbi.nlm.nih.gov/pubmed/30537688
   Lim, Seng Han, et al. “Three-Dimensional Printing of a Microneedle Array on Personalized Curved Surfaces for Dual-Pronged Treatment of Trigger Finger.” Biofabrication, U.S. National Library of Medicine, 10 Jan. 2017, www.ncbi.nlm.nih.gov/pubmed/28071597.: www.ncbi.nlm.nih.gov/pubmed/28071597



 Project #2:  DNA Polymerase at the Single-Molecule Level
Faculty Mentors:  
Professor Gregory Weiss Chemistry

Professor Philip CollinsPhysics & Astronomy

Description:  Background:

The replication of DNA is required for life. DNA polymerases are structurally reminiscent of a hand; the enzyme twists into a right-hand fold. The “fingers” accept incoming nucleotides, the “thumb” securely grasps the duplex DNA, and the “palm” comprises the active site. Various polypeptide domains undertake various activities: polymerase, primase, lyase, ATPase, exonuclease, and helicase. Each of these specialized activities can independently impact the processivity, polymerization speed, and fidelity of the polymerase.

DNA polymerase (Φ29) replicates the Φ29 bacteriophage1 genome and engages in terminal protein deoxynucleotidylation2, as well as some degenerative processes.3,4 Φ29 is mainstay in molecular biology and biotechnology due to its remarkable processivity in the relative absence of ancillary proteins4 and seamless coupling of strand displacement with polymerization processes.5,6 The diverse, central role that Φ29 adopts makes it a prospective candidate for studies attempting to elucidate nearly imperceptible fluttering in protein domains as the polymerase incorporates nucleotides.

Most of the studies are conducted with bulk quantities of enzymes, generating population- level results; these studies neglect the subtle dynamics of structural conformations that may have vast ramifications on enzymatic activity.7 Single-molecule studies may resolve transient enzymatic motions that accompany DNA synthesis and elucidate mechanisms behind nucleotide recognition and catalytic speed. Previous studies of the Klenow fragment, a substantial protein artifact in E. coli responsible for fragmentary DNA synthesis, manifested the molecular recognition and base pair specificity tendencies of this enzyme.

Our lab in collaboration with the Collin’s lab invented a single-molecule technique called bio-nanocircuits. This approach relies on tethering a pyrene maleimide linker to a single, surface cysteine residue on a DNA polymerase. The pyrene maleimide linker is then linked to a single- walled carbon nanotube through non-covalent bonding. As the DNA polymerase undergoes conformational changes, the electrostatic charges of side chains perturb the current running through the carbon nanotube. These electronic signatures may be statistically analyzed to elucidate single molecule properties: time spent in open conformation, typical incorporation event durations, and erratic pausing behaviors.

Project Description:

This project represents an expansion of the multifaceted exploration of Φ29 single- molecule dynamics. Currently, we have characterized Φ29 and are algorithmically analyzing fluctuations in the bio-nanocircuits to uncover single-molecule dynamics. We seek to expand our efforts to DNA polymerases, such as Taq polymerase from Thermus aquaticus and RB69 from the Family B polymerases. Taq polymerase is ubiquitous enzyme used to exponentially duplicate DNA strands through polymerase chain reactions; RB69 is another Family B polymerase,8 which touts decreased processivity but greater fidelity.10 Single-molecule studies may delineate the counterbalanced properties of processivity and polymerization speed in each of these polymerases.

The overarching goal is to conduct single-molecule studies on DNA polymerases that resolve hidden dynamics and generate results distinct from population level studies. Particular questions involve understanding the domain motions that corresponded to balancing speed and processivity for the DNA polymerases. Understanding the mechanical compromises between speed and processivity would forge progress in molecular biology and DNA sequencing applications. Protein engineering might increase the efficacy of single-molecule sequencing efforts and having holistic tabulations of polymerase properties may allow companies to selectively discern the protein candidates for further sequencing technologies.

Students’ Involvement and Expected Outcomes:

Students will conduct mutagenesis of Φ29 to eliminate extraneous attachment sites; previously prepared plasmids for other DNA polymerases (example...?) will also be utilized. Plasmids will be verified via DNA Sanger sequencing prior to protein overexpression and purification. Protein variants will be expressed in E. coli and purified using Ni-NTA and size exclusion chromatography. After obtaining pure product, student will characterize samples for further enzymatic activity assays. Ensemble activity assays will be designed and executed by student using ssDNA templates, and the results will be rendered and compared to previous Φ29 variants.

Expected outcomes:

Discoveries may construct a more robust model of Φ29 catalytic activity and perhaps extricate meaningful patterns from the apparently stochastic movements of the molecule. Constructing a more lucid model of Φ29 catalytic activity will enable specific customization and selectivity for DNA polymerase applications in DNA sequencing. Nascent technologies founded on such discoveries may also make sequencing technologies more accessible, potentially leading to widespread applications in the medical field.

Prerequisites: Any undergraduate from any STEM discipline and any level may pursue this research

Recommended Web sites and publications: 
   (1) Salas, M. (1991) Protein-priming of DNA replication. Annu. Rev. Biochem., 60(1), 39–71.

(2) Blanco L., Salas M. (1996) Relating structure to function in Φ29. J. Biol. Chem., 271(1), 8509–12.

(3) Esteban, J. A., Salas, M., Blanco, L. (1993) Fidelity of phi 29 DNA polymerase. Comparison between protein-primed initiation and DNA polymerization. J. Biol. Chem., 268(4), 2719 –26.

(4) Blanco, L., Bernad, A., Lázaro, J. M., Mártin, G., Garmendia, C., Salas, M. (1989) Highly efficient DNA synthesis by the phage Φ29 DNA polymerase. J. Biol. Chem., 264(15), 8935-40.

(5) Chen, A., Gui, G. F., Zhuo, Y., Chai Y. Q., Xiang, Y., Yuan, R. (2015) Signal-off Electrochemiluminescence Biosensor Based on Phi29 DNA Polymerase Mediated Strand Displacement Amplification for MicroRNA Detection. Anal. Chem., 87(12), 6328-34.

(6) Vega, M., Lázaro, J. M., Salas, M., et. al. (1996) Primer-terminus stabilization at the 3'-5' exonuclease active site of Phi29 DNA polymerase. EMBO J., 15(5), 182-92.

(7) Schwartz, J. J. and Quake, S. R. (2010) Single molecule measurement of the “speed limit” of DNA polymerase. Proc. Natl. Acad. Sci. 107(3), 1254.

(8) Xia, S., Konigsberg, W. H. (2014) RB69 DNA polymerase structure, kinetics, and fidelity. Biochemistry, 53(17), 2752-67.

(9) Pugliese, K. M., Gul, O. T., Choi, Y., Olson, T. J., Sims, P. C., Collins, P. G., Weiss, G. A. (2015) Processive Incorporation of Deoxynucleoside Triphosphate Analogs by Single- Molecule DNA Polymerase I (Klenow Fragment) Nanocircuits. J. Am. Chem. Soc., 137(30), 9587-94.

(10) Tchesnokov,E.P.,Obikhod,A.,Schinazi,R.F.,Götte,M.(2009) Engineeringofa chimeric RB69 DNA polymerase sensitive to drugs targeting the cytomegalovirus enzyme. J. Biol. Chem., 284(39), 26439-46.:



 Project #3:  Fate of Antibiotic Resistant Genes in Human Sewage
Faculty Mentors:  
Professor Chenyang Sunny JiangCivil & Environmental Engineering

Professor Katrine WhitesonMolecular Biology & Biochemistry

Description:  The rapid emergence of antibiotic resistant bacteria has raised serious public health concerns because of the progressively severe ramifications in the treatment of infectious diseases. Each year in the United States, approximately 2 million people acquire an antibiotic-resistant infection, and at least 23,000 of these cases are lethal (CDC, 2013). The antimicrobial resistance crisis has been ascribed to the intensive use of antibiotics in medical treatment, veterinary and agriculture applications. That prevalent use of antimicrobial agents has largely intervened in the selection and spread of antibiotic-resistant bacteria. While the occurrence of antibiotic resistance has mainly studied in clinical, community, and agricultural settings, the possible role of the environment in dissemination route of resistance has been increasingly recognized in recent years. Human sewage has been considered as a major source of antibiotic resistance genes because it creates an environment that favors the emergence of antibiotic-resistant bacteria by increasing the interactions between residual antibiotics from humans and the enormous sewage microbial population. Therefore, municipal wastewater treatment plants, as a critical step to process human sewage, deserves people’s attention. However, the variability of resistant profile and the efficacy of degradation of resistance genes in different treatment plants have not been well characterized.

This project will examine sewage microbiome at three different wastewater treatment plants in Washington D.C., Los Angeles, and Orange County. Sewage samples from different treatment processes will be collected. Bacteria will be concentrated and genomic DNA extracted. Samples will be analyzed using Next Generation Sequencing and metagenomic methods. Bioinformatic software will be used to understand the signature of the sewage microbiome. Analysis of resistant profiles of different wastewater treatment plants will provide an insight of antibiotic resistant genes occurrence in municipal wastewaters for better understanding on the fate of antibiotic resistant genes through human sewage. Such knowledge is important to assist in the management of antibiotic resistance emission through sewage discharge.

Students’ Involvement:

The students will review literature on the topic. They will analyze data and interpret the results using bioinformatics software that they learn throughout the project. Students will be responsible for attending a regular weekly meeting with the mentors and the research team to update their progress and discuss their ideas. Students participating in the project are required to contribute on average 10 hours per week of time.

Students’ Expected Outcomes:

Students will:
• Collaborate in an interdisciplinary team environment with leaders in the field.
• Get hands-on experience with bioinformatics resources and tools relevant in metagenomics data analysis.
• Understand fundamentals of genomic sequencing, wastewater treatment processes, and the connection between human and environment.
• Learn how to write a research report in the appropriate scientific style.

Prerequisites: Students should have fundamental knowledge of biology and computer programing. Backgrounds in computer language and programming are strongly desired.

Recommended Web sites and publications: 
   Dila, D. K., Newton, R. J., Vineis, J. H., et al. (2015). Sewage Reflects the Microbiomes of Human Populations. MBio, 6(2), 1–9. https://doi.org/10.1128/mbio.02574-14: https://doi.org/10.1128/mbio.02574-14
   Quince, C., Walker, A. W., Simpson, J. T., Loman, N. J., & Segata, N. (2017). Supplement-study design Shotgun metagenomics, from sampling to analysis. Nature Biotechnology, 35(9), 833–844. https://doi.org/10.1038/nbt.3935: https://doi.org/10.1038/nbt.3935
   Guo, J., Li, J., Chen, H., Bond, P. L., & Yuan, Z. (2017). Metagenomic analysis reveals wastewater treatment plants as hotspots of antibiotic resistance genes and mobile genetic elements. Water Research, 123, 468–478. https://doi.org/https://doi.org/10.1016/j.watres.2017.07.002: https://doi.org/https://doi.org/10.1016/j.watres.2017.07.002



 Project #4:  Multidisciplinary Applications of Machine Learning towards Identifying Social Conventions
Faculty Mentors:  
Dr. Sergio Gago-MasagueComputer Science

Professor Louis NarensCognitive Sciences

Dr. Kimberly JamesonInstitute for Mathematical Behavioral Sciences

Professor Jean-Paul CarvalhoEconomics

Description:  Additional Mentors:
Maryam Gooyabadi, Graduate Student
Institute of Mathematical Behavioral Sciences

Project Description:

This project is a multidisciplinary approach that brings together computational methods in machine learning to aid quantitative methods in the social sciences towards the study of social conventions.

Recent advancements in machine learning has yet to be applied in the social sciences where it can advance current quantitative methods in Sociology, Anthropology, Linguistics, and Economics. Specifically, it can help identify groups with unique underlying properties in order to gain insight into various types of social conventions – linguistic, cultural, and moral. Social conventions can be thought of as ideas and behaviors that are shared between members of groups, from simple ways of greeting to more abstract notions such as justice.

Recently conventions have been mathematically modeled and in this project we develop new computational approaches to this modeling in order to answer: How many such groups exist and how can we identify them when researcher has only cultural knowledge about these groups? One aspect of this research is to understand whether cultural knowledge matches scientific knowledge obtained through machine learning. Another question that will be addressed is, Do people who believe they are experts in a field are actual experts?

This project will utilize methods in unsupervised machine learning, specifically where the number of groups is unknown. Said differently: in the case where data from thousands of anonymous individuals on various dimensions exists, unsupervised machine learning can cluster individuals based on similarity (i.e. give us the number of groups present in our data) for us to study each group’s unique convention.

Students’ Involvement and Expected Outcomes:

Students will have the unique opportunity to learn sophisticated computational and quantitative methodologies from Computer Science and the Social Sciences. Topics include: unsupervised machine learning (probabilistic clustering algorithms), networks, cultural consensus model, Multidimensional analysis, Factorial analysis, and evolutionary game theory. Through their experience, students can expect to have a working knowledge of:

● Various types of Machine learning: supervised, semi supervised, and unsupervised
● Implementation of algorithms for clustering data from anonymous participants in python
● Implementation of quantitative methods in social sciences
● Survey design and data gathering
● Write about the processes, procedures, findings, and implications of such research
● Creative graphics for displaying the results which can include
● Create evolving network


Prerequisites: Individuals with strong computing skills, mathematics or statistics background, with a working knowledge of computer programming languages and algorithm design, interests in STEM careers (science, technology, engineering, and mathematics). Innovative thinkers with real-world experiences or relevant experience are encouraged to apply. Knowledge of Python or other computing languages.

Recommended Web sites and publications: 
   Samuel J. Gershmana,, David M. Blei (2011) “A tutorial on Bayesian nonparametric models” Journal of Mathematical Psychology

Mengrui Ni, Erik B. Sudderth, Mike Hughes (2015) “Variational Inference for Beta-Bernoulli DirichletProcess Mixture Models”

Marina Meila, Harr Chen (2010) “Dirichlet Process Mixtures of Generalized Mallows Models”

Maryam Gooyabadi, Kirbi Joe (2017) “Further evolution of natural categorization systems: An approach to evolving color concepts”
:



 Project #5:  Optical Measurement System for Characterizing Vaping Smoke
Faculty Mentors:  
Professor Michael T. KleinmanCommunity & Environmental Medicine

Professor Derek Dunn-RankinMechanical & Aerospace Engineering

Professor Yu-Chien ChienMechanical & Aerospace Engineering

Description:  E-cigarettes and vaping, once touted as a panacea for smoking cessation, have come under intense scrutiny as acute reactions in smokers have recently caused illness and even death. The causes of these reactions are actively being studied, including using animal exposure models. The Centers for Disease Control and Prevention point out that the specific compounds or ingredients causing the reactions are not yet known, but whatever the ingredient, the delivery mechanism is a vapor condensate. The size distribution of this condensate cloud dictates where in the lung and how much of the vaping material deposits and hence which tissues are affected.

This MDP project is to design a flow system that incorporates an existing optical particle sizing device to characterize the physical size distribution of e-cigarettes under different use conditions and with different vaping delivery devices. By linking the physical properties of the vapor cloud to the device producing that vapor it will be possible to separate physical impact effects from chemical ones.

Students’ Involvement and Expected Outcomes:

Students will learn the basics of evaporation/condensation processes in e-cigarettes. They will understand fundamental properties of vapor clouds and how optical light scattering can be used to measure those properties. With this information, they will design a flow measurement system that can be adapted to an existing optical measurement device. They will then build and test the flow system for a range of e-cigarettes and use the particle size distribution data to predict how changing generation parameters will alter the amount and location of material deposited in the lung using a computer model (MPPD). If their device is sufficiently accurate, it will be implemented as part of an animal exposure configuration where the smoke will be measured as it is generated before its delivery to test how differences in vapor aerosol characteristics influences health outcomes.

Prerequisites: Open to all graduate and undergraduate students, but those with an interest in engineering design, flows, and health effects of aerosols are particularly welcome. Skills in data acquisition and experimentation will be useful.

Recommended Web sites and publications: 
   Blair, S.L., S.A. Epstein, S.A. Nizkorodov, and N. Staimer: A Real-Time Fast-Flow Tube Study of VOC and Particulate Emissions from Electronic, Potentially Reduced-Harm, Conventional, and Reference Cigarettes. Aerosol Sci Technol 49(9): 816-827 (2015): https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4696598/
   Kleinman, M., A. Keebaugh, D. Herman, L. Wingen, and N. Staimer: Can reactions between ozone and organic constituents of ambient particles influence PM-induced adverse cardiovascular health effects? Abstracts of Papers of the American Chemical Society 254(2017):
   Cassee, F.R., H. Muijser, E. Duistermaat, J.J. Freijer, K.B. Geerse, J.C.M. Marijnissen et al.: Particle size-dependent total mass deposition in lungs determines inhalation toxicity of cadmium chloride aerosols in rats. Application of a multiple path dosimetry model. Archives of Toxicology 76(5-6): 277-286 (2002).: https://link.springer.com/article/10.1007%2Fs00204-002-0344-8
   Envirometrix Instruments LLC: http://www.emi-djh.com/Products-Berkeley-CA.html



 Project #6:  Predictors of Patient Experience in the Pediatric Setting
Faculty Mentors:  
Professor Michelle FortierAnesthesiology

Professor Zeev KainAnesthesiology

Description:  Patient satisfaction in hospital care is of utmost importance to not only the patients, but also for the hospitals as well.1 Based on the work of Berwick and other, in 2013, the Centers for Medicare and Medicaid Services initiated the hospital Value-Based Purchasing (VBP) program that changed hospitals reimbursements to be based on hospital’s specific performance data.2,3 This novel approach has challenged medical institutions to deliver high-quality care and reduce costs. There are four equally scored domains of a hospital’s VBP score: safety, clinical care, cost reduction, and patient experience. Since 25% of a hospital’s VBP score is measured by patient experience, hospitals have been increasingly investing in strategies to improve patient related experience and satisfaction.4 The availability and growing focus towards evaluating patient experience data should prompt health care institutions to explore critical factors that may improve overall health outcomes and care. Patient satisfaction is not only important to hospital rankings but also important to physicians on an individual level. Patients now have the opportunity to look around for healthcare services as they have more choices for healthcare options which leads to hospitals having an obligation to improve their satisfaction scores for earning patient’s loyalty to their organization. Previous studies in adult populations have shown that patients that highly rate their healthcare experience are more likely to return to the organization and comply with the physician’s recommendations.

Much of the current literature in patient satisfaction focuses primarily in the adult population. The literature in patient satisfaction in pediatric settings is limited, but has shown that some of the predictors of patient satisfaction in pediatric settings is include wait times, patient-provider communication, and office staff cheerfulness. 6,7,8,9 Various adult patient experience studies have looked at demographic variables, such as race and age, as determinants for patient satisfaction, and have found a correlation between different races and patient satisfaction ratings.10 Empathy of the physician in adult patient populations has also shown to have an relative effect on patient satisfaction scores. Those who believed their physicians were more empathetic were more likely to have higher patient satisfaction scores.11 While studies in the adult patient population indicate the importance of demographic characteristics, these and other personality and behavioral traits have not been considered in the pediatric patient population. There is a need to understanding the key drivers of patient experience in hospitals that can be used to generalize overall patient experience to improve scores for healthcare institutions that depend on the VBP score for funding.

The overall purpose of the study is to prospectively identify clinical and demographic and behavioral predictors of patient satisfaction scores in a pediatric surgery and oncology patient population. The hypothesis is that the key predictors will have a significant impact on patient satisfaction scores.

Specific aim 1: To examine if demographic variables such as age and gender are affecting patient satisfaction scores.

Specific aim 2: To examine if personality characteristics of parent and child and physician are affecting patient satisfaction scores.

Students’ Involvement and Expected Outcomes:

In collaboration with our multidisciplinary team, students involved in this research project will get hands-on experience conducting research in a clinical setting with a diverse patient population. Students will work with the PIs and project coordinator to review medical charts, approach and recruit eligible families to participate in this study, administer questionnaires, and organize data for quantitative analyses and presentation. Students will be expected to independently communicate with hospital personnel, patients, and families in a professional, respectful manner, and will actively work to maintain patient confidentiality.

Prerequisites: Undergraduate and graduate students interested in participating must apply to become a part of the UCI Center on Stress & Health. Eligibility includes a 3.00 GPA and a commitment of 10 hours per week.

Recommended Web sites and publications: 
   1. Iannuzzi JC, Kahn SA, Zhang L, Gestring ML, Noyes K, Monson JRT. ScienceDirect Getting satisfaction : drivers of surgical Hospital Consumer Assessment of Health care Providers and Systems survey scores. J Surg Res. 2015;197(1):155-161. doi:10.1016/j.jss.2015.03.045

2. Tsai TC, Orav EJ, Jha AK. Patient satisfaction and quality of surgical care in US hospitals. 2015;261(1):2-8. doi:10.1097/SLA.0000000000000765

3. Berwick DM, Nolan TW, Whittington J. The Triple Aim: Care, Health, And Cost. Health Aff. 2008;27(3):759-769. doi:10.1377/hlthaff.27.3.759

4. Anhang Price R, Elliott MN, Zaslavsky AM, et al. Examining the Role of Patient Experience Surveys in Measuring Health Care Quality. Med Care Res Rev. 2014;71(5):522-554. doi:10.1177/1077558714541480

5. Review P. Determinants of patient satisfaction : a systematic review. 2017;137(2):89-101. doi:10.1177/1757913916634136

6. Fustino NJ, Wohlfeil M, Smith HL. Determination of Key Drivers of Patient Experience in a Midsize Pediatric Hematology-Oncology Ambulatory Clinic. 2018:332-338. doi:10.31486/toj.18.0091

7. Davis J, Burrows JF, Ben Khallouq B, Rosen P. Predictors of Patient Satisfaction in Pediatric Oncology. J Pediatr Oncol Nurs. 2017;34(6):435-438. doi:10.1177/1043454217717239

8. Brenn BR, Choudhry DK, Sacks K. Outpatient outcomes and satisfaction in pediatric population: data from the postoperative phone call. Lerman J, ed. Pediatr Anesth. 2016;26(2):158-163. doi:10.1111/pan.12817

9. Peng FB, Burrows JF, Shirley ED, Rosen P. Unlocking the Doors to Patient Satisfaction in Pediatric Orthopaedics. 2018;38(8):398-402. doi:10.1097/BPO.0000000000000837

10. Goldstein E, Elliott MN, Lehrman WG, Giordano LA. Racial / Ethnic Differences in Patients ’ Perceptions of Inpatient Care Using the HCAHPS Survey. 2010:74-92.

11. Mann RK, Siddiqui Z, Kurbanova N, Qayyum R, Qayyum R. Effect of HCAHPS reporting on patient satisfaction with physician communication. J Hosp Med. 2016;11(2):105-110. doi:10.1002/jhm.2490: