IT-based Patient Interventions for Opioid Abuse: Evaluation using Analytical Model

The number of people in the US with opioid abuse exceeds 2 million and the total cost is approximately $100B per year. In this study, we focus on patient-level interventions and present three IT-based interventions: (a) mobile reminders, (b) electronic monitoring, and (c) composite intervention. We have developed an analytical model for evaluating interventions using Return-on-Investment (ROI). The interventions are cost-effective for higher values of intervention effectiveness, hospital, and emergency room cost. However, with QoL improvement, cost-effectiveness improves significantly. We also explored the use of financial incentives for increasing the adoption of interventions. These results will help patients, healthcare professionals, decision-makers, and family members to choose the most suitable intervention to address opioid abuse

Study to assess knowledge attitude and practices of antenatal care among antenatal women attending outdoor clinic in tertiary care hospital

Background: Maternal mortality rate in India continues to be a national challenge despite of the various measures taken by the Indian government, Non profit organizations in and outside the country including the World Health Organization. To find out the gaps between the providers and beneficiaries we tried to find out what actually prevents our pregnant women to seek Regular Antenatal Care by evaluating their knowledge, attitudes and practices towards antenatal care.Methods: All antenatal women attending outpatient clinic of department of obstetrics and gynae Gandhi medical college Bhopal over a period of one year were included in the study. Study group was of unbooked antenatal women and control group consisted of booked women at the hospital. All subjects were given a predesigned, pretested questionnaire to fill in their local language and the data thus obtained was analysed statistically.Results: 86.16% subjects visited ANC clinic during first trimester, 66.33% knew correctly about frequency of antenatal visits, 97.50% knew about Tetanus immunization. Likewise, 78.33% had positive attitude towards antenatal checkups and early registration. Similarly, 70.4% took adequate antenatal care, 93.33% took iron folic acid tablets.Conclusions: Thus, the study shows that the knowledge, attitude and practice of antenatal care is good in the booked subject the same is not the case in unbooked subjects coming to the hospital with complications or being referred to the hospital

Synthetic routes to new core/shell nanogels:design and application in biomaterials

A very interesting class of nanoparticles extensively used for bio-applications is that of hydrogel particles, also called nanogels. There is an increasing interest in the design of hydrogel nanoparticles that have biofunctionality for applications in cell targeting, drug delivery, and biomedicine. The dissertation focuses on developing synthetic strategies for making diverse hydrogel nanoparticles customized to have desirable properties for various bio-applications. We have also investigated the potential of such nanoparticles as coatings for biomedical implants. Chapter 1 gives a brief introduction to hydrogel nanoparticles and the properties that make them attractive for various applications. The details of the syntheses of well defined, stable nanoparticles, commonly used in literature, are described in Chapter 2. Chapter 3 describes our synthesis of hollow sub-50 nm nanogels, which are otherwise difficult to synthesize based on the strategy discussed in Chapter 2. Chapter 4 also demonstrates how simple strategies borrowed from organic chemistry help in producing nanogels with multiple functionalities that are otherwise difficult to obtain, which also is an important advance over the synthetic methods discussed in Chapter 2. Chapter 5 describes how a general strategy based on photoaffinity labeling can yield materials with many applications ranging from optical materials, drug delivery, to biosensing. The latter part of the dissertation describes applications of various nanogels in biology especially as coatings that can control inflammation caused by biomaterials. Chapter 6 describes a method to functionalize flexible biomaterials with the nanogels, thus enabling in vivo investigations of the nanogels as potential coatings for controlling inflammation. Chapter 7 describes the biological studies performed (in collaboration with Garcia Group in the School of Mechanical Engineering at Georgia Tech) on various nanogels, aimed towards obtaining the most functional and efficient materials for implant applications. Chapter 8 describes application of hollow nanogels for covalently immobilizing biomolecules. This chapter also demonstrates how simple non-functional materials can be made unique and functional by means of traditional organic reactions. Finally, in order to broaden the applications of nanogel based materials.Ph.D.Committee Chair: Prof. L. Andrew Lyon; Committee Member: Prof. Laren Tolbert; Committee Member: Prof. Marcus Weck; Committee Member: Prof. Niren Murthy; Committee Member: Prof. Seth Marde

Smart Interventions for Effective Medication Adherence

In this research we present a model for medication adherence from information systems and technologies (IS/IT) perspective. Information technology applications for healthcare have the potential to improve cost-effectiveness, quality and accessibility of healthcare. To date, measurement of patient medication adherence and use of interventions to improve adherence are rare in routine clinical practice. IS/IT perspective helps in leveraging the technology advancements to develop a health IT system for effectively measuring medication adherence and administering interventions. Majority of medication adherence studies have focused on average medication adherence. Average medication adherence is the ratio of the number of doses consumed and the number of doses prescribed. It does not matter in which order or pattern patients consume the dose. Patients with enormously diverse dosing behavior can achieve the same average levels of medication adher­ence. The same outcomes with different levels of ad­herence raise the possibility that patterns of adherence affect the effectiveness of medication adherence. We propose that medication adherence research should utilize effective medication adherence (EMA), derived by including both the pattern and average medication adherence for a patient. Using design science research (DSR) approach we have developed a model as an artifact for smart interventions. We have leveraged behavior change techniques (BCTs) based on the behavior change theories to design smart intervention. Because of the need for real time requirements for the system, we are also focusing on hierarchical control system theory and reference model architecture (RMA). The benefit of using this design is to enable an intervention to be administered dynamically on a need basis. A key distinction from existing systems is that the developed model leverages probabilistic measure instead of static schedule. We have evaluated and validated the model using formal proofs and by domain experts. The research adds to the IS knowledge base by providing the theory based smart interventions leveraging BCTs and RMA for improving the medication adherence. It introduces EMA as a measurement of medication adherence to healthcare systems. Smart interventions based on EMA will further lead to reducing the healthcare cost by improving prescription outcomes

Smart Interventions for Effective Medication Adherence

In this research we present a model for medication adherence from information systems and technologies (IS/IT) perspective. Information technology applications for healthcare have the potential to improve cost-effectiveness, quality and accessibility of healthcare. To date, measurement of patient medication adherence and use of interventions to improve adherence are rare in routine clinical practice. IS/IT perspective helps in leveraging the technology advancements to develop a health IT system for effectively measuring medication adherence and administering interventions. Majority of medication adherence studies have focused on average medication adherence. Average medication adherence is the ratio of the number of doses consumed and the number of doses prescribed. It does not matter in which order or pattern patients consume the dose. Patients with enormously diverse dosing behavior can achieve the same average levels of medication adher­ence. The same outcomes with different levels of ad­herence raise the possibility that patterns of adherence affect the effectiveness of medication adherence. We propose that medication adherence research should utilize effective medication adherence (EMA), derived by including both the pattern and average medication adherence for a patient. Using design science research (DSR) approach we have developed a model as an artifact for smart interventions. We have leveraged behavior change techniques (BCTs) based on the behavior change theories to design smart intervention. Because of the need for real time requirements for the system, we are also focusing on hierarchical control system theory and reference model architecture (RMA). The benefit of using this design is to enable an intervention to be administered dynamically on a need basis. A key distinction from existing systems is that the developed model leverages probabilistic measure instead of static schedule. We have evaluated and validated the model using formal proofs and by domain experts. The research adds to the IS knowledge base by providing the theory based smart interventions leveraging BCTs and RMA for improving the medication adherence. It introduces EMA as a measurement of medication adherence to healthcare systems. Smart interventions based on EMA will further lead to reducing the healthcare cost by improving prescription outcomes

Impact of endomycorrhizal fungi and other bioinoculants on growth enhancement of Glycine max (L.) Merrill

In the present investigation, the contributions of two indigenous arbuscular mycorrhizal fungi (Glomus mosseae and Acaulospora laevis), along with Trichoderma viride and Bradyrhizobium japonicum on growth parameters of Soybean, Glycine max (L.) Merrill were investigated. The results obtained indicated the dependence of soybean on mycorrhizal symbiosis. The different growth parameters increased significantly after 120 days of inoculation in comparison to control. Among all the growth parameters studied, plant height (162±3.34), fresh shoot weight (31.26±1.45), dry shoot weight (3.52±0.05), fresh root weight (4.07±0.56), dry root weight (1.03±0.03), root length (49.0±4.47) and leaf area (32.58±1.70) were highest in the combination of G. mosseae + A. laevis + T. viride + B. japonicum but arbuscular mycorrhizal (AM) spore number (95.2±3.19) and percent mycorrhizal root colonization (93.26±3.96) were maximum in single inoculation of G. mosseae. Second most effective results were observed in the plants treated with G. mosseae alone. Thus the presence of arbuscular mycorrhizal fungi (AMF) and other bioinoculants in rhizosphere of soybean had positive effect on the different growth parameters