Electronic Health Records: A Promising New Way To Fight the Opioid Epidemic

From the injured athlete who underwent ACL surgery to the mother recovering from a broken ankle, the nation’s opioid epidemic selects its victims indiscriminately and at an alarming rate, with 8% to 12% of patients prescribed painkillers eventually developing a use disorder. As of 2017 – the most recent full year for which data is available – prescribing rates still hover around 60 prescriptions per 100 persons.  Addiction disorders disproportionately affect young adults, with opioid overdoses accounting forone in every five deaths of Americans between the ages of 25 and 34. This is the grim face of the epidemic today.

While the Attorney General and U.S. courts fight to determine the role pharmaceutical companies such as Purdue Pharma had in precipitating our nation’s drug crisis – as well as their prior knowledge of the addictiveness of drugs like OxyContin – patients, physicians and local communities continue to battle the epidemic on the front lines. From controversial measures like safe injection sites, to collection programs for unused pills as well as new medical policies designed to reduce overprescribing, communities across the country are developing and implementing innovative ways to address the epidemic.

One of the strategies outlined in recent months comes from the Centers for Medicare & Medicaid Services (CMS), which describes a three-pronged approach to fighting the epidemic: opioid use disorder (OUD) prevention, treatment and improved data utilization in electronic health records (EHRs). The cornerstone of this strategy – to equip health workers on the front lines with access to real-time data about patient prescribing patterns – is a key aspect of CMS’s roadmap that health technology leaders are still exploring today. And, while much of the necessary data is already being collected through various means, getting it in front of prescribers when and where they need it – and in a useable format – is another matter.

The full Forbes article can be viewed at this link.  

Patients’ Engagement in Medical Education

Patient engagement in medical education for health care professionals is fundamental. Patients are effective and successful in teaching skills to medical and nursing students with positive outcomes on students and patients. Patients diagnosed with chronic illnesses can be considered as "expert patients" who can bring in their experience of illness and teach the emotional, psychological, social, and economic aspects of illness.

Traditionally, patients have been actively involved in teaching and/or assessment which is often achieved by a trained patient-educator with the goal of bringing patient voices into the education about illness and its impact on patient lifestyle and personal and psychological well-being alongside socioeconomic status.

The full article can be downloaded below.  

Primary Care Clinician Adherence to Specialist Advice in Electronic Consultation

Electronic consultation (eConsult) services can improve access to specialist advice. Little is known, however, about whether and how often primary care clinicians adhere to the advice they receive. We evaluated how primary care clinicians use recommendations conveyed by specialists via the Champlain BASE (Building Access to Specialists through eConsultation) eConsult service and how eConsult affects clinical management of patients in primary care.

This is a descriptive analysis based on a retrospective chart audit of 291 eConsults done between January 20, 2017 and August 31, 2017 at the Bruyère Family Health Team, located in Ottawa, Canada. Patients’ charts were reviewed until 6 months after specialist response for the following main outcomes: implementation of specialist advice by primary care clinicians, communication of the results to the patients, method, and time frame of communication.

Primary care clinicians adhered to specialist advice in 82% of cases. Adherence ranged from 62% to 93% across recommendation categories. Questions asked by primary care clinicians related to diagnosis (63%), management (27%), drug treatment (10%), and procedures (1%). Recommendations of the eConsult were communicated to patients in 79% of cases, most often by face-toface visit (38%), telephone call (32%), or use of the patient portal (9%). Communication occurred in a median of 5 days.

We found little evidence of barriers to implementing specialist advice with use of eConsult, which suggests recommendations given through service were actionable. With a high primary care clinician adherence to specialist recommendations and primary care clinician–to-patient communication, we conclude that eConsult delivers good-quality care and improves patient management.

The full article can be downloaded below.  

Visit Planning Using a Waiting Room Health IT Tool: The Aligning Patients and Providers Randomized Controlled Trial

Time during primary care visits is limited. We tested the hypothesis that a waiting room health information technology (IT) tool to help patients identify and voice their top visit priorities would lead to better visit interactions and improved quality of care.

We designed a waiting room tool, the Visit Planner, to guide adult patients through the process of identifying their top priorities for their visit and effectively expressing these priorities to their clinician. We tested this tool in a cluster-randomized controlled trial with usual care as the control. Eligible patients had at least 1 clinical care gap (eg, overdue for cancer screening, suboptimal chronic disease risk factor control, or medication nonadherence).

The study (conducted March 31, 2016 through December 31, 2017) included 750 English- or Spanish-speaking patients. Compared with usual care patients, intervention patients more often reported “definitely” preparing questions for their doctor (59.5% vs 45.1%, P <.001) and “definitely” expressing their top concerns at the beginning of the visit (91.3% vs 83.3%, P=.005). Patients in both arms reported high levels of satisfaction with their care (86.8% vs 89.9%, P=.20). With 6 months of follow-up, prevalence of clinical care gaps was reduced by a similar amount in each study arm.

A simple waiting room–based tool significantly improved visit communication. Patients using the Visit Planner were more prepared and more likely to begin the visit by communicating their top priorities. These changes did not, however, lead to further reduction in aggregate clinical care gaps beyond the improvements seen in the usual care arm.

The full article can be downloaded below.  

Health Care Technology Predictions For 2019

In 2019, health care information technology (HIT) in the U.S. will continue to be transformed by external forces from around the world. To be honest, the whole of health care is feeling the pain of this evolution, and there are challenges that need to be met head-on.

But there are also inklings of light at the end of the tunnel. The digital transformation of this sector is only in the embryonic stages, but there’s clear evidence of enormous development and growth on the horizon. Here are my top five predictions for health care technology in 2019.

1. There Will Be A Major Push Toward Truly Digitized Health Care
2. AI Will Start To Penetrate The Broader Health Care IT Landscape
3. The Transition From Data Centers To The Cloud Will Accelerate
4. Cybersecurity Attacks Will Continue To Escalate
5. The Mobile-First Movement Will Gain More Traction

The full Forbes article can be viewed at this link.  

HOW EMPLOYERS ARE FIXING HEALTH CARE

For competitive companies, providing quality coverage is good business. It helps attract and retain employees (good health plans are a sought-after benefit), and workers who receive good, affordable care are more satisfied and productive. But that coverage is expensive, and costs are rising. Employer spending on health care services increased by 44% per enrollee from 2007 to 2016, reaching an annual amount of nearly $700 billion in 2017 — roughly what the Pentagon spends on defense. Walmart alone spends billions of dollars a year on health care for its associates (as the company refers to its employees).

Much is at stake: Various actors in the health care ecosystem, some large insurers and providers among them, benefit from an arrangement that layers on administrative costs and rewards volume, not value. Yet business as usual is unsustainable for those absorbing the costs and experiencing the uneven quality of care. Pioneering employers and providers are in a position to upend the status quo and change expectations about what affordable, quality care can and should be. What follows is an account of our experience with one important effort, among several being tried, to find a better way.

The full Harvard Business Review article can be found at this link.  

AI Will Not Replace Doctors, But It May Drastically Change Their Jobs

There’s strong consensus that AI won’t replace doctors. Arguing that it can goes not just against the complexity of what doctors actually do, but such a stance fails to realize the need for a human touch. You will want a human to hold your hand when discussing your cancer diagnosis. Empathy is critical in such life-changing moments. The level of human connection you’ll have with your doctor will directly influence how well you feel, how likely you are to stick with a treatment plan, and how you and your family will remember the trauma for decades to come.

But agreeing that the human doctor will always be there doesn’t reflect the massive changes and risks to their jobs. Doctors essentially do three things: diagnosis (what’s wrong with me?), treatment (what’s the plan?) and prognosis (how long before it gets better?). All three core tasks are being gradually performed by AI systems that employ machine learning, deep learning, natural language processing and time series forecasting.

The full Forbes article can be read at this link.  

Why hospitals shouldn’t use telepresence robots to deliver devastating news

A doctor speaking through a telepresence robot in California gave a dying patient and his family devastating medical news, raising questions about when the convenience of telemedicine should be skipped in favor of a face-to-face conversation.

The full article from The Verge can be viewed at this link.  

Interoperability Progress and Remaining Data Quality Barriers of Certified Health Information Technologies

The Consolidated Clinical Document Architecture (C-CDA) is the primary standard for clinical document exchange in the United States. While document exchange is prevalent today, prior research has documented challenges to high quality, effective interoperability using this standard. Many electronic health records (EHRs) have recently been certified to a new version of the C-CDA standard as part of federal programs for EHR adoption. This renewed certification generated example documents from 52 health information technologies that have been made publicly available. This research applies automated tooling and manual inspection to evaluate conformance and data quality of these testing artifacts. It catalogs interoperability progress as well as remaining barriers to effective data exchange. Its findings underscore the importance of programs that evaluate data quality beyond schematron conformance to enable the high quality and safe exchange of clinical data.

The full article can be downloaded below.

ScriptNumerate: A Data-to-Advice Pipeline using Compound Digital Objects to Increase the Interoperability of Computable Biomedical Knowledge

Many obstacles must be overcome to generate new biomedical knowledge from real-world data and then directly apply the newly generated knowledge for decision support. Attempts to bridge the processes of data analysis and technical implementation of analytic results reveal a number of gaps. As one example, the knowledge format used to communicate results from data analysis often differs from the knowledge format required by systems to compute advice. We asked whether a shared format could be used by both processes. To address this question, we developed a data-to-advice pipeline called ScriptNumerate. ScriptNumerate analyzes historical e-prescription data and communicates its results in a compound digital object format. ScriptNumerate then uses these same compound digital objects to compute its advice about whether new e-prescriptions have common, rare, or unprecedented instructions. ScriptNumerate demonstrates that data-to-advice pipelines are feasible. In the future, data-to-advice pipelines similar to ScriptNumerate may help support Learning Health Systems.

The full article can be downloaded below.