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

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

Effect of socio demographic and personal factors on infertility and its association with mental health and social support in North Indian population

Background: Motherhood is a bliss in women’s lives and thus infertility is considered as a social stigma. In present study, we measure the effect of socio-demographic and personal factors on infertility as well as the status of social support and mental health in infertile women.Methods: This was a cross-sectional study comprising 90 infertile women and 90 women in the control group, conducted at Ram Prakash Gupta Memorial Mother and Child State Referral Centre of RMLIMS, Lucknow, India. The questionnaires used were Socio-demographic and personal characteristics, General Health Questionnaire (GHQ-12) to assess mental health, Perceived Social Support Questionnaire (PSSQ). To determine the relationship between socio-demographic characteristics, personal characters, mental health, and social support with infertility, Chi-square test was used.Results: Age, education, and occupation had statistically significant (p<0.05) relation with infertility, high BMI, and caffeine intake also had the significant adverse effect on fertility outcomes (p<0.05). Infertility patients had poor mental health status (higher mean GHQ-12 score 20.8±4.2) and poor perceived social support (lower mean PSSQ score 29.7±7.0).Conclusions: In present study, the socio-demographic factors, lifestyle factors, social support and mental health status is associated with fertility outcomes. Their modifications have the potential to improve reproductive performances. A structured programme of education, social support, and counselling by specialist health professionals should be formulated to improve the quality of life as well as fertility outcomes in infertile patients

Early prediction of pregnancy induced hypertension by colour Doppler and role of antioxidants

Background: Hypertension is one of the commonest medical complications during pregnancy and a leading cause of maternal and perinatal mortality. The objective of this study was to evaluate the efficacy of color Doppler in predicting pregnancy induced hypertension at early gestation by assessing blood flow velocity profile and to assess the role of antioxidants in reducing the oxidative stress of the disease by their effect on pregnancy outcome.Methods: The study was carried as a blind prospective study in 310 antenatal patients attending the outpatient department and indoor cases of upper India sugar exchange maternity hospital, GSVM Medical College, Kanpur, Uttar Pradesh, India for a period of 18 months.Results: Early trimester colour Doppler ultrasonography has an excellent role to play as a predictor of pregnancy induced hypertension. However the role of antioxidant supplementation in early pregnancy for amelioration of the process could not be justified.Conclusions: From the above study it is concluded that early trimester colour Doppler ultrasonography has an excellent role to play as a predictor of pregnancy induced hypertension

Opioid Use Disorder: Studying Quality of Life with IT-based Interventions

Opioid Use Disorder (OUD) has become a major public health challenge. There have been several interventions, including those based on health-IT, proposed recently. There is a major need to study these interventions. We are interested in exploring how different IT-based interventions impact opioid related Quality of Life. We developed a model using Markov chain for three different states in OUD. The model and results can lead to better decision making by healthcare professionals, patients and insurance companies. More specifically, the proposed model and results can help in (a) whether to prescribe opioids to different types of patients, (b) what IT-based interventions are suitable with an opioid prescription, and (c) how patients and healthcare professionals can select an intervention out of multiple available interventions

Opioid Use Disorder: Decision Support for Healthcare Professionals

Opioid Use Disorder (OUD) is defined as deviating from the physician’s prescription of a specific opioid. The OUD patients will require expensive inpatient treatment followed by a long-term outpatient treatment. We present a decision support system for opioid prescriptions, inpatient treatment (detoxification), and outpatient treatment by healthcare professionals. We analyzed the impact of inaccuracy in PDMP, decision scenarios, and effectiveness of decisions in outpatient scenarios on the opioid resource requirements. The proposed DSS will lead to better decision making using both the risk score and patient’s condition

CPCP violation in b→sℓℓb \to s \ell \ell: a model independent analysis

We perform a model-independent global fit to all germane and updated $b \to s \ell \ell$ ($\ell=e,\,\mu$) data assuming new physics couplings to be complex. Under the approximation that new physics universally affects muon and electron sectors and that either one or two related operators contribute at a time, we identify scenarios which provide a good fit to the data. It turns out that the favored scenarios remain the same as obtained for the real fit. Further, the magnitude of complex couplings can be as large as that of their real counterparts and these are reflected in the predictions of the direct $CP$ asymmetry, $A_{\rm CP}$, in $B \to (K,\, K^*) \mu^+ \mu^-$ along with a number of angular $CP$ asymmetries, $A_i$, in $B^0 \to K^{*0} \mu^+ \mu^-$ decay. The sensitivities of these observables to various solutions are different in the low and high-$q^2$ bins. We also determine observables which can serve as unique identifier for a particular new physics solution. Moreover, we examine correlations between $A_{\rm CP}$ and several $A_i$ observables. A precise measurement of $A_{\rm CP}$ and $A_i$ observables can not only confirm the existence of additional weak phases but can also enable unique determination of Lorentz structure of possible new physics in $b \to s \mu^+ \mu^-$ transition.Comment: 15 pages, 4 figures; updated results in view of December 2022 LHCb measurement