The Rural Health Safety Net Under Pressure: Rural Hospital Vulnerability 

This analysis was developed by The Chartis Center for Rural Health and designed to model the probability of closure for all rural hospitals as a function of various indicators of closure and provide new insight into the underlying characteristics of hospitals that are more vulnerable to closure.

The full analysis can be downloaded below.  

The Prognostic Significance of Quantitative Myocardial Perfusion: An Artificial Intelligence Based Approach Using Perfusion Mapping

Myocardial perfusion reflects the macro- and microvascular coronary circulation. Recent quantitation developments using cardiovascular magnetic resonance (CMR) perfusion permit automated measurement clinically. We explored the prognostic significance of stress myocardial blood flow (MBF) and myocardial perfusion reserve (MPR, the ratio of stress to rest MBF).

A two center study of patients with both suspected and known coronary artery disease referred clinically for perfusion assessment. Image analysis was performed automatically using a novel artificial intelligence approach deriving global and regional stress and rest MBF and MPR. Cox proportional hazard models adjusting for co-morbidities and CMR parameters sought associations of stress MBF and MPR with death and major adverse cardiovascular events (MACE), including myocardial infarction, stroke, heart failure hospitalization, late (>90 day) revascularization and death.

1049 patients were included with median follow-up 605 (interquartile range 464-814) days. There were 42 (4.0%) deaths and 188 MACE in 174 (16.6%) patients. Stress MBF and MPR were independently associated with both death and MACE. For each 1ml/g/min decrease in stress MBF the adjusted hazard ratio (HR) for death and MACE were 1.93 (95% CI 1.08-3.48, P=0.028) and 2.14 (95% CI 1.58-2.90, P<0.0001) respectively, even after adjusting for age and co-morbidity. For each 1 unit decrease in MPR the adjusted HR for death and MACE were 2.45 (95% CI 1.42-4.24, P=0.001) and 1.74 (95% CI 1.36-2.22, P<0.0001) respectively. In patients without regional perfusion defects on clinical read and no known macrovascular coronary artery disease (n=783), MPR remained independently associated with death and MACE, with stress MBF remaining associated with MACE only.

In patients with known or suspected coronary artery disease, reduced MBF and MPR measured automatically inline using artificial intelligence quantification of CMR perfusion mapping provides a strong, independent predictor of adverse cardiovascular outcomes.

The full article can be viewed at this link.  

Why health care AI can’t replace medicine’s human component

The AMA deliberately uses the term augmented intelligence (AI)—rather than the more common term “artificial intelligence”—when referring to machine-learning computer algorithms that hold the potential to produce dramatic breakthroughs for health care research, population health risk-stratification and diagnostic support.

And there’s a good reason for that.

“In health care, machines are not acting alone but rather in concert and in careful guidance with humans, i.e., us—physicians,” said AMA Board of Trustees Chair Jesse M. Ehrenfeld, MD, MPH. “There is and will continue to be a human component to medicine, which cannot be replaced. AI is best optimized when it is designed to leverage human intelligence.”

The full AMA article can be viewed at this link.  

Application of Blockchain to Maintaining Patient Records in Electronic Health Record for Enhanced Privacy, Scalability, and Availability

Electronic Health Record (EHR) systems are increasingly used as an effective method to share patients’ records among different hospitals. However, it is still a challenge to access scattered patient data through multiple EHRs. Our goal is to build a system to access patient records easily among EHRs without relying on a centralized supervisory system.

We apply consortium blockchain to compose a distributed system using Hyperledger Fabric incorporating existent EHRs. Peer nodes hold the same ledger on which the address of a patient record in an EHR is written. Individual patients are identified by unique certificates issued by a local certificate authorities that collaborate with each other in a channel of the network. To protect a patient’s privacy, we use a proxy re-encryption scheme when the data are transferred. We designed and implemented various chaincodes to handle business logic agreed by member organizations of the network.

We developed a prototype system to implement our concept and tested its performance including chaincode logic. The results demonstrated that our system can be used by doctors to find patient’s records and verify patient’s consent on access to the data. Patients also can seamlessly receive their past records from other hospitals. The access log is stored transparently and immutably in the ledger that is used for auditing purpose.

Our system is feasible and flexible with scalability and availability in adapting to existing EHRs for strengthening security and privacy in managing patient records. Our research is expected to provide an effective method to integrate dispersed patient records among medical institutions.

The full article can be downloaded below.  

How Technology, Medicine And At-Home Devices Can Improve Healthcare Access And Cost

Healthcare is changing. After years of stagnation and inadequate innovations, the call for care that is higher quality and more accessible and that costs less is beginning to be answered. We're starting to see incremental progress toward meaningful healthcare technology and reimagined delivery models. New developments in digital medicine, DIY care and AI are emerging, with the potential to advance the industry in ways that previous attempts have failed.

Despite signs of progress, doctor's office wait times continue to rise. Middle- and low-income patients are in critical need of more affordable primary and specialty care. Across the country, critical access and other rural hospitals are closing at an alarming rate, leaving people in those areas struggling to find the time, transportation and money needed to see a physician. Primary care visits are declining, while our overall population health continues to lag behind most developed countries.

These issues are the impetus for momentum in digital medicine and direct-to-consumer healthcare. Consumers today expect more from all of the services they use, and healthcare is no exception. New, niche providers and technology focused on patient experience are setting a new standard for healthcare delivery. Some solutions—those that offer unprecedented convenience alongside real medical expertise—have the potential to improve outcomes.

The full Forbes article can be viewed at this link.  

Problems Paying Medical Bills, 2018

In the United States, the percentage of all persons who were in families having problems paying medical bills decreased 5.5 percentage points from 19.7% in 2011 to 14.2% in 2018. In this same year, the percentage of persons who were in families having problems paying medical bills varied by sex, age, race and ethnicity, and health insurance status. Among persons of all ages, the percentage who were in families having problems paying medical bills was higher among females, children aged 0–17 years, and non-Hispanic black persons than among males, adults, and other racial and ethnic groups, respectively. Among persons under age 65, the percentage having problems paying medical bills was highest among those who were uninsured, followed by those who were covered with Medicaid and private health insurance. Among adults aged 65 and over, those with Medicare and Medicaid, and Medicare only had similar percentages of having problems paying medical bills; both were higher than those with Medicare Advantage and private coverage.

The full NCHS Data Brief can be downloaded below.

How Algorithmic Empathy Will Improve Health Care

Recently, health care providers have started using artificially intelligent chat bots to guide patients through normal intake processes and other functions normally performed by staff. The bots can ask patients about basic symptoms, verify health insurance, and follow up after visits. They have the great potential to increase access and reduce costs for health care providers. But one of the key challenges to their successful adoption is maintaining patient engagement with these software robots; how do you get patients to talk to software robots about sensitive medical data? It turns out the answer is empathy.

The full Forbes article can be viewed at this link.  

Tele-transitions of care (TTOC): a 12-month, randomized controlled trial evaluating the use of Telehealth to achieve triple aim objectives

Poor transitions of care leads to increased health costs, over-utilization of emergency room departments, increased re-hospitalizations and causes poor patient experiences and outcomes. This study evaluated Telehealth feasibility in improving transitions of care.

This is a 12-month randomized controlled trial, evaluating the use of telehealth (remote patient monitoring and video visits) versus standard transitions of care with the primary outcomes of hospital readmission and emergency department utilization and secondary outcomes of access to care, medication management and adherence and patient engagement. Electronic Medical Record data, Health Information Exchange data and phone survey data was collected. Multi-variable logistic regression models were created to evaluate the effect of Telehealth on hospital readmission, emergency department utilization, medication adherence. Chi-square tests or Fisher’s exact tests were used to compare the percentages of categorical variables between the Telehealth and control groups. T tests or Wilcoxon rank sum tests were used to compared means and medians between the two randomized groups.

The study conducted between June 2017 and 2018, included 102 patients. Compared with the standard of care, Telehealth patients were more likely to have medicine reconciliation (p = 0.013) and were 7 times more likely to adhere to medication than the control group (p = 0.03). Telehealth patients exhibited enthusiasm (p = 0.0001), and confidence that Telehealth could improve their healthcare (p = 0.0001). Telehealth showed no statistical significance on emergency department utilization (p = 0.691) nor for readmissions (p = 0.31). 100% of Telehealth patients found the intervention to be valuable, 98% if given the opportunity, reported they would continue using telehealth to manage their healthcare needs, and 94% reported that the remote patient monitoring technology was useful.

Telehealth can improve transitions of care after hospital discharge improving patient engagement and adherence to medications. Although this study was unable to show the effect of Telehealth on reduced healthcare utilization, more research needs to be done in order to understand the true impact of Telehealth on preventing avoidable hospital readmission and emergency department visits.

The full article can be downloaded below.  

For The First Time Ever, A Drug Developed By AI Will Be Tested In Human Trials

In a world first, a medicine developed by artificial intelligence may be used to treat patients with obsessive-compulsive disorder. The news is remarkable and hints that in the future, AI may help drug development become faster and more efficiently than ever before. 

The first non-man made drug molecule, DSP-1181, has now entered Phase 1 clinical trials, European Pharmaceutical Review reported. The molecule is a long-acting potent serotonin 5-HT1A receptor agonist and was developed using AI that was the product of a partnership between Japan’s Sumitomo Dainippon Pharma and Exscientia in the UK. The compound was developed in a remarkable time, with AI able to complete in 12 months what typically takes five years. 

The full Forbes article can be viewed at this link.