Medication-related harm in older adults following hospital discharge: development and validation of a prediction tool

Participants were followed up for 8 weeks in the community by senior pharmacists to identify MRH (adverse drug reactions, harm from non-adherence, harm from medication error). Three data sources provided MRH and healthcare use information: hospital readmissions, primary care use, participant telephone interview. Candidate variables for prognostic modelling were selected using two systematic reviews, the views of patients with MRH and an expert panel of clinicians. Multivariable logistic regression with backward elimination, based on the Akaike Information Criterion, was used to develop the PRIME tool. The tool was internally validated.

1116 out of 1280 recruited participants completed follow-up (87%). Uncertain MRH cases (’possible’ and ’probable’) were excluded, leaving a tool derivation cohort of 818. 119 (15%) participants experienced ’definite’ MRH requiring healthcare use and 699 participants did not. Modelling resulted in a prediction tool with eight variables measured at hospital discharge: age, gender, antiplatelet drug, sodium level, antidiabetic drug, past adverse drug reaction, number of medicines, living alone. The tool’s discrimination C-statistic was 0.69 (0.66 after validation) and showed good calibration. Decision curve analysis demonstrated the potential value of the tool to guide clinical decision making compared with alternative approaches.

The PRIME tool could be used to identify older patients at high risk of MRH requiring healthcare use following hospital discharge. Prior to clinical use we recommend the tool’s evaluation in other settings.

The full article can be downloaded below.  

Reconceptualising precision public health

As currently conceived, precision public health is at risk of becoming precision medicine at a population level. This paper outlines a framework for precision public health that, in contrast to its current operationalisation, is consistent with public health principles because it integrates factors at all levels, while illuminating social position as a fundamental determinant of health and health inequities. We review conceptual foundations of public health, outline a proposed framework for precision public health and describe its operationalisation within research and practice. Social position shapes individuals’ unequal experiences of the social determinants of health. Thus, in our formulation, precision public health investigates how multiple dimensions of social position interact to confer health risk differently for precisely defined population subgroups according to the social contexts in which they are embedded, while considering relevant biological and behavioural factors. It leverages this information to uncover the precise and intersecting social structures that pattern health outcomes, and to identify actionable interventions within the social contexts of affected groups. We contend that studies informed by this framework offer greater potential to improve health than current conceptualisations of precision public health that do not address root causes. Moreover, expanding beyond master categories of social position and operationalising these categories in more precise ways across time and place can enrich public health research through greater attention to the heterogeneity of social positions, their causes and health effects, leading to the identification of points of intervention that are specific enough to be useful in reducing health inequities. Failure to attend to this level of particularity may mask the true nature of health risk, the causal mechanisms at play and appropriate interventions. Conceptualised thus, precision public health is a research endeavour with much to offer by way of understanding and intervening on the causes of poor health and health inequities.

The full article can be downloaded below.  

Can Synthetic Biology Make Insulin Faster, Better and Cheaper?

Rising insulin prices have become a dangerous norm for diabetics. Whether on Twitter, TV, or public radio, the insulin market has the world’s attention — and for all the wrong reasons. 

Insulin prices are not only skyrocketing (they increased by 99% from 2012 to 2016); lack of access is killing Americans. People with Type 1, Type 2, and even gestational diabetes need to take insulin to regulate their blood sugar levels. Insulin is the signal that tells our cells to take in glucose and convert it to energy. Without this molecule, glucose builds up in the bloodstream and can cause serious complications, including cardiovascular disease. 

We are at the precipice of a profound public health crisis. If insulin has been around for nearly fifty years, and production has become cheaper over time, how did we get here? And what can synthetic biology do about these circumstances? 

The full Forbes article can be viewed at this link.  

InsightRX Raises $10 Million To Personalize Drug Doses

On September 20, InsightRX, a precision medicine company, announced the closing of a $10 million Series A funding round led by HealthX Ventures with participation from Rock Health, OSF Healthcare, Leawood Venture Capital, Premier Inc. and previous investor GreatPoint Ventures.

Using patient-specific data, quantitative pharmacology models, and what is known as Bayesian forecasting, InsightRX have built software to help clinicians prescribe optimal doses of powerful medications, increasing the likelihood of the most therapeutic benefit with the least side effects. InsightRX’s software is currently in use across more than 100 drug and therapeutic areas, including oncology and infectious diseases and through this approach, hospitals, care teams and patients could realize a number of benefits.

The full Forbes article can be viewed at this link.  

Population Health Vs. Personalized Medicine: Lost In Translation?

Evidence-based medicine, it seems commonsensical; who could argue about using the best evidence available to make treatment decisions? The difficulty, of course, is that the evidence comes from studies of large populations, often expressed in terms of average responses; and as a clinician, you want to tailor the care to the one member of the population in front of you, your patient. How do you reconcile population-based evidence with the desire for personalized care?

The full article from the American Council on Science and Health can be viewed at this link.  

Primary Care Is Integral To Improving U.S. Healthcare, But Is Falling Further Behind

Judging from healthcare advertisements on the Internet, television, and radio, specialists rule the roost as healthcare providers in the U.S. healthcare system. From orthopedic surgeons to cancer centers to ophthalmologists, it's hard to avoid the onslaught of advertising for specialist care. In the U.S., primary care is not featured nearly as prominently in advertising or the media.

But, increasing primary care access is seen as essential to improving population health. Effective primary care can help reduce emergency room visits and hospitalizations. However, in most U.S. states, primary care spending represents 5%-7% of total healthcare expenditures, which is less than half of the 14% average in Western European countries.

The federal government estimates that approximately 84 million Americans presently lack adequate access to primary care.

The full Forbes article can be viewed at this link.  

Apple Announced Three New Healthcare Studies And Now Is The Time To Ask Hard Questions

On September 10, 2019, Apple announced that it would be launching “three unprecedented medical studies” that will allow Apple Watch users to “contribute to potential medical discoveries and help create the next generation of innovative health products.”

When a much-loved company such as Apple announces an innovative new project, takes on elite university partners and medical organizations, and promises “groundbreaking” new medical research that will “help patients today and … make contributions that will benefit future generations,” it automatically sounds like a positive “disruption” in a flawed industry. The flurry of celebratory press releases from university partners make it seem like we’re finally getting a handle on health data and with the help of our greatest minds. There’s lots of money flying around right now and everyone is excited.

But right now we have the opportunity to ask a question we often throw out in a tongue-in-cheek manner: what could possibly go wrong?

The full Forbes article can be viewed at this link.  

Deprescribing: Practical Ways to Support Person-Centred, Evidence-Based Deprescribing

Deprescribing is complex and multifactorial with multiple approaches described in the literature. Internationally, there are guidelines and tools available to aid clinicians and patients to identify and safely withdraw inappropriate medications, post a shared decision-making medicines optimisation review. The increase in available treatments and use of single disease model guidelines have led to a healthcare system geared towards prescribing, with deprescribing often seen as a separate activity. Deprescribing should be seen as part of prescribing, and is a key element in ensuring patients remain on the most appropriate medications at the correct doses for them. Due to the complex nature of polypharmacy, every patient experience and relationship with medications is unique. The individual’s history must be incorporated into a patient-centred medication review, in order for medicines to remain optimal through changes in circumstance and health. Knowledge of the law and appropriate recording is important to ensure consent is adequately gained and recorded in line with processes followed when initiating a medication. In recent years, with the increase in interested clinicians globally, a number of prominent networks have grown, creating crucial links for both research and sharing of good practice.

The full article can be downloaded below.  

The 3 Causes Of Physician Burnout (And Why There’s No Simple Solution)

Burnout is a big and burgeoning problem in the United States. According to a recent Mayo Clinic report, it affects 28% of the general working population. Among physicians, however, the rate is markedly higher, ranging from 44% to 54% in most studies. 

More concerning are the consequences: Doctors who report burnout symptoms are twice as likely to commit a medical error. They’re also twice as likely as their patients to commit suicide. 

Though the physician “burnout crisis” has left many in the profession battered, bruised and pleading for help, there has been little noticeable improvement in recent years. To understand this strange division—between the urgency of the problem and the lack of effective solutions—I’ve surveyed patients and fellow physicians, scoured the latest research, news coverage and social media commentary. 

Most often, doctors look at burnout as a problem with a single etiology (cause). Instead, burnout has three distinct causes:  

1. The healthcare system 
2. Moral injury 
3. Medical culture 

The majority of physicians who believe burnout is tied to just one cause (particularly to a broken healthcare system) also believe that the solutions are beyond their control. In reality, each of these causes requires attention and action from doctors. Until that happens, the troublesome symptoms of physician burnout will only intensify. 

The full Forbes article can be viewed at this link.  

Nonlinear Systems in Healthcare Towards Intelligent Disease Prediction

Healthcare is one of the key fields that works quite strongly with advanced analytical techniques for prediction of diseases and risks. Data being the most important asset in recent times, a huge amount of health data is being collected, thanks to the recent advancements of IoT, smart healthcare, etc. But the focal objective lies in making sense of that data and to obtain knowledge, using intelligent analytics. Nonlinear systems find use specifically in this field, working closely with health data. Using advanced methods of machine learning and computational intelligence, nonlinear analysis performs a key role in analyzing the enormous amount of data, aimed at finding important patterns and predicting diseases. Especially in the field of smart healthcare, this chapter explores some aspects of nonlinear systems in predictive analytics, providing a holistic view of the field as well as some examples to illustrate such intelligent systems toward disease prediction.

The full piece can be downloaded below.