Medicaid Best Buys: Improving Care Management for High-Need, High-Cost Beneficiaries

Outlines steps in designing a comprehensive management program for Medicaid beneficiaries with complex needs, including targeting and prioritizing beneficiaries, tailoring interventions, assessing results, and structuring supportive financing

Processing of Electronic Health Records using Deep Learning: A review

Availability of large amount of clinical data is opening up new research avenues in a number of fields. An exciting field in this respect is healthcare, where secondary use of healthcare data is beginning to revolutionize healthcare. Except for availability of Big Data, both medical data from healthcare institutions (such as EMR data) and data generated from health and wellbeing devices (such as personal trackers), a significant contribution to this trend is also being made by recent advances on machine learning, specifically deep learning algorithms

Hill Physicians Medical Group: Independent Physicians Working to Improve Quality and Reduce Costs

Describes how a group of independent physicians improved clinical outcomes through an innovative incentive system -- combining pay-for-performance and fee-for-service -- implemented with quality improvement processes. Discusses lessons learned

360 Quantified Self

Wearable devices with a wide range of sensors have contributed to the rise of the Quantified Self movement, where individuals log everything ranging from the number of steps they have taken, to their heart rate, to their sleeping patterns. Sensors do not, however, typically sense the social and ambient environment of the users, such as general life style attributes or information about their social network. This means that the users themselves, and the medical practitioners, privy to the wearable sensor data, only have a narrow view of the individual, limited mainly to certain aspects of their physical condition. In this paper we describe a number of use cases for how social media can be used to complement the check-up data and those from sensors to gain a more holistic view on individuals' health, a perspective we call the 360 Quantified Self. Health-related information can be obtained from sources as diverse as food photo sharing, location check-ins, or profile pictures. Additionally, information from a person's ego network can shed light on the social dimension of wellbeing which is widely acknowledged to be of utmost importance, even though they are currently rarely used for medical diagnosis. We articulate a long-term vision describing the desirable list of technical advances and variety of data to achieve an integrated system encompassing Electronic Health Records (EHR), data from wearable devices, alongside information derived from social media data.Comment: QCRI Technical Repor

Personal Health Technology: CPN based Modeling of Coordinated Neighborhood Care Environments (Hubs) and Personal Care Device Ecosystems

Healthcare supported by mobile devices, or “mHealth,” has rapidly emerged as a very broad ecosystem that can empower safer, more affordable, and more comfortable independent living environments and assist residents to age in place with a variety of well-understood chronic diseases. mHealth ecosystems leverage every available type of regulated medical and consumer-grade Patient Care Devices (or PCDs). mHealth technologies can also support innovative care and reimbursement models like the Patient-Centered Medical Home (PCMH) and Accountable Care Organizations (ACOs). Although consumer-grade PCDs are becoming ubiquitous, they typically do not provide a large variety of integrated system options for care coordination beyond single individuals. Understanding how to safely implement and use those devices to support heterogeneous mixes of patients, illnesses, devices, medications, and situations in neighborhood contexts is still a case-by-case challenge. By utilizing a well-formalized Colored Petri Nets (CPNs) based approach, this paper provides a proof-of-concept simulation framework for modeling and designing coordinated community care hubs

Does health-related fitness influence health status?

Background The health-related fitness model (HRFM) was created to integrate five components of physical fitness (cardiorespiratory fitness, muscular strength, muscular endurance, flexibility, and body composition) known to support positive health. Research has evaluated individual health-related fitness markers relative to various biometric and perception measures; however, the HRFM has not been used to establish an overall fitness score. The purpose of this research was to create and examine an overall fitness score relative to individual health-related fitness markers, self-reported quality of life (QOL), biometric markers, and disease risk factors. Methods Health risk appraisals (HRA) were performed at three midwest manufacturing and processing companies. The HRA collected participant demographic and medical information, biometric measures, physical fitness, and QOL. The physical fitness assessments consisted of easy to administer protocols for the five markers of health-related fitness. Individual health-related fitness markers were used to create an overall fitness score (0-5 points) with a pass/fail system based on normative categorization tables. Descriptive statistics, Spearman correlations, and non-parametric tests were used to examine individual fitness markers, overall pass/fail fitness and QOL. Analysis of Variance (ANOVA) with Tukey\u27s post hoc was used to detect differences in biometric measures by overall pass/fail fitness score and individual fitness markers. Likelihood modeling explored the predictive significance of overall/pass fail fitness score and individual fitness marker categorizations relative to coronary heart disease (CHD) and metabolic syndrome risk factors. Results A total of 176 participants between the ages of 20 to 76 years participated in the HRA. Gender distribution was essentially equal with 48.3% male (n=85) and 51.7% female (n=91). The majority of participants were of white ethnicity (94.3%) and reported education status of at least a high school degree (96.7%). Participants demonstrated low overall pass/fail fitness with 81.8% of participants passing fewer than two of the individual physical fitness assessments. Overall pass/fail fitness reflected each of the five health-related fitness markers through significant positive correlations (r=.34-.52; p\u3c0.01), significant distribution differences (p\u3c0.01), and significantly similar gender distribution differences (p\u3c0.05). QOL also demonstrated similar distributions between overall pass/fail fitness and various fitness markers (cardiorespiratory fitness, muscular strength, and flexibility) while exhibiting a negative correlation (r = -0.27; p\u3c0.01) and significantly different distribution (p\u3c0.05) with body composition. Low density lipoproteins (LDL) were significantly different (p Conclusion Results suggest an overall pass/fail fitness score is able to comprehensively reflect individual health-related fitness markers. An overall pass/fail fitness score may also serve as a successful outlet in understanding the relationship between health-related fitness and QOL. However, overall pass/fail fitness score was unable to distinguish chronic disease risk factors and minimal predictive associations were seen between normative categorizations and risk factors. These findings suggest the overall pass/fail fitness score reflects individual health-related fitness markers and QOL, but does not serve as a reference for understanding disease risk

Computational Content Analysis of Negative Tweets for Obesity, Diet, Diabetes, and Exercise

Social media based digital epidemiology has the potential to support faster response and deeper understanding of public health related threats. This study proposes a new framework to analyze unstructured health related textual data via Twitter users' post (tweets) to characterize the negative health sentiments and non-health related concerns in relations to the corpus of negative sentiments, regarding Diet Diabetes Exercise, and Obesity (DDEO). Through the collection of 6 million Tweets for one month, this study identified the prominent topics of users as it relates to the negative sentiments. Our proposed framework uses two text mining methods, sentiment analysis and topic modeling, to discover negative topics. The negative sentiments of Twitter users support the literature narratives and the many morbidity issues that are associated with DDEO and the linkage between obesity and diabetes. The framework offers a potential method to understand the publics' opinions and sentiments regarding DDEO. More importantly, this research provides new opportunities for computational social scientists, medical experts, and public health professionals to collectively address DDEO-related issues.Comment: The 2017 Annual Meeting of the Association for Information Science and Technology (ASIST

Wellness Intervention as a Quality of Life Predictor in Mentally Ill Veterans

Veterans with serious mental illness (SMI) are at high risk of developing conditions such as insulin resistance, obesity, and smoking, which may lead to chronic medical problems. As a result, the morbidity and mortality of people with SMI are high compared to the general population. It appears that integrated care improves the wellbeing of veterans; however, there is a gap in the literature on wellness-based interventions for veterans with SMI. The purpose of this cross-sectional study was to evaluate the association between a wellness intervention for veterans and their perceived quality of life (QOL). Social cognitive theory was the theoretical lens through which this study was conducted. It was hypothesized that there is an association between veterans’ involvement in the wellness component of a program and their perceived QOL. The program is a specialty VA service known as Mental Health Intensive Case Management (MHICM). A total of 112 veterans served by a single MHICM program in the U.S. Southeast completed a validated VA survey that measures health related QOL. A chart audit was conducted to gather information such as years served by the program and type of wellness services received. Regression modeling was used to assess the relationship between a veteran’s involvement in the wellness interventions and his or her perceived QOL. The study results showed that the interventions were not significant predictors of veterans QOL. Two covariates, age and gender, were found to be significant predictors, but each accounted for less than 7% of the variance. The study findings show the need for further research to explore the role of wellness interventions in a veteran’s recovery. Social change may result from encouraging veterans with SMIs to participate in self-rated QOL measures

Phenomenological Assessment of Integrative Medicine Decision-making and the Utility of Predictive and Prescriptive Analytics Tools

The U.S. Healthcare system is struggling to manage the burden of chronic disease, racial and socio-economic disparities, and the debilitating impact of the current global pandemic caused by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2). More patients need alternatives to allopathic or “Western” medicine focused on fighting disease with mechanism, pharmaceuticals, and invasive measures. They are seeking Integrative Medicine which focuses on health and healing, emphasizing the centrality of the patient-physician relationship. In addition to providing the best conventional care, IM focuses on preventive maintenance, wellness, improved behaviors, and a holistic care plan. This qualitative research assessed whether predictive and prescriptive analytics (artificial intelligence tools that predict patient outcomes and recommend treatments, interventions, and medications) supports the decision-making processes of IM practitioners who treat patients suffering from chronic pain. PPA was used in a few U.S. hospitals but was not widely available for IM practitioners at the time of this research. Phenomenological interviews showed doctors benefit from technology that aggregates data, providing a clear patient snapshot. PPA exposed historical information that doctors often miss. However, current systems lacked the design to manage individualized, holistic care focused on the mind, body, and spirit. Using the Future-Focused Task-Technology Fit theory, the research suggested PPA could actually do more harm than good in its current state. Future technology must be patient-focused and designed with a better understanding of the IM task and group characteristics (e.g., the unique way providers practice medicine) to reduce algorithm aversion and increase adoption. In the ideal future state, PPA will surface healthcare Big Data from multiple sources, support communication and collaboration across the patient’s support system and community of care, and track the various objective and subjective factors contributing to the path to wellness