The association between medication non‐adherence and adverse health outcomes in ageing populations: A systematic review and meta‐analysis

The aim of this systematic review and meta‐analysis was to synthesise the evidence relating to medication non‐adherence and its association with health outcomes in people aged ≥50 years.

Seven databases were searched up to February 2019 for observational studies that measured medication (non‐)adherence as a predictor of the following health outcomes in adults aged ≥50 years: healthcare utilisation (hospitalisation, emergency department visits, outpatient visits and general practitioner visits), mortality, adverse clinical events and quality of life. Screening and quality assessment using validated criteria were completed by 2 reviewers independently. Random effects models were used to generate pooled estimates of association using adjusted study results. The full methodological approach was published on PROSPERO (ID: CRD42017077264).

Sixty‐six studies were identified for qualitative synthesis, with 11 of these studies eligible for meta‐analyses. A meta‐analysis including 3 studies measuring medication non‐adherence in adults aged ≥55 years showed a significant association with all‐cause hospitalisation (adjusted odds ratio 1.17, 95% confidence interval [CI] 1.12, 1.21). A meta‐analysis including 2 studies showed that medication non‐adherence was not significantly associated with an emergency department visit (adjusted odds ratio 1.05, 95% CI 0.90, 1.22). Good adherence was associated with a 21% reduction in long‐term mortality risk in comparison to medication non‐adherence (adjusted hazard ratio 0.79, 95% CI 0.63, 0.98).

Medication non‐adherence may be significantly associated with all‐ cause hospitalisation and mortality in older people. Medication adherence should be monitored and addressed in this cohort to minimise hospitalisation, improve clinical outcomes and reduce healthcare costs.

The full article can be downloaded below.  

The personalized medicine challenge: shifting to population health through real-world data

Personalized medicine (PM) is an initiative aimed at optimizing a person’s health through targeted, precise care. The field of precision health has rapidly blossomed, fed by the fertile, data-rich healthcare environment and the hype surrounding artificial intelligence (AI) and big data analytics (BDA). The ability to sequence and analyze large amounts of omics data (e.g., genomics, proteomics), enhanced by AI algorithms, has encouraged the growth of targeted therapies (Jameson and Longo 2015). Enormous databases are now fed by real-world data (RWD), automatically generated healthcare data based on records of routine medical encounters. These databases inform analyses from drug discovery to disease relapse prediction (Mc Cord et al. 2018).

The full editorial can be downloaded below.  

Why An Aging Population Means Healthcare Customer Experience Must Adapt

America is getting older, and the healthcare system is buckling under the pressure. The double whammy of people generally living longer and the massive Baby Boomer generation creating the “largest-ever population of older adults in America” has necessitated a level of experience innovation in the healthcare industry that simply has no precedent.  

Thankfully, there are a lot of brilliant minds working on a solution to this problem, identifying and addressing the needs of health consumers, medical providers and insurers all at once in order to create a smoother system for all.

In the final article of a three-part series, I interviewed three leaders in the healthcare industry for whom addressing the experience of the aging population is a top priority. 

The full Forbes article can be viewed at this link.  

Prediction of premature all-cause mortality: A prospective general population cohort study comparing machine-learning and standard epidemiological approaches

Prognostic modelling using standard methods is well-established, particularly for predicting risk of single diseases. Machine-learning may offer potential to explore outcomes of even greater complexity, such as premature death. This study aimed to develop novel prediction algorithms using machine-learning, in addition to standard survival modelling, to predict premature all-cause mortality.

A prospective population cohort of 502,628 participants aged 40–69 years were recruited to the UK Biobank from 2006–2010 and followed-up until 2016. Participants were assessed on a range of demographic, biometric, clinical and lifestyle factors. Mortality data by ICD-10 were obtained from linkage to Office of National Statistics. Models were developed using deep learning, random forest and Cox regression. Calibration was assessed by comparing observed to predicted risks; and discrimination by area under the ‘receiver operating curve’ (AUC).

14,418 deaths (2.9%) occurred over a total follow-up time of 3,508,454 person-years. A simple age and gender Cox model was the least predictive (AUC 0.689, 95% CI 0.681–0.699). A multivariate Cox regression model significantly improved discrimination by 6.2% (AUC 0.751, 95% CI 0.748–0.767). The application of machine-learning algorithms further improved discrimination by 3.2% using random forest (AUC 0.783, 95% CI 0.776–0.791) and 3.9% using deep learning (AUC 0.790, 95% CI 0.783–0.797). These ML algorithms improved discrimination by 9.4% and 10.1% respectively from a simple age and gender Cox regression model. Random forest and deep learning achieved similar levels of discrimination with no significant difference. Machine-learning algorithms were well-calibrated, while Cox regression models consistently over-predicted risk.

Machine-learning significantly improved accuracy of prediction of premature all-cause mortality in this middle-aged population, compared to standard methods. This study illustrates the value of machine-learning for risk prediction within a traditional epidemiological study design, and how this approach might be reported to assist scientific verification.

The full article can be downloaded below.  

Assessing the Unintended Consequences of Health Policy on Rural Populations and Places 

Because of the complexity of the U.S. health care system, thoughtfully designed health policies carry a risk of having unintended consequences, particularly for health systems in rural places that have place-based fundamentals that deviate substantially from urban and suburban areas. Policies developed without consideration of rural contexts are likely to create unanticipated and negative consequences for rural residents, providers, and communities.

When health policies are being developed, a number of themes that emerge are useful to keep in mind. Specifically, how will this policy impact the ability of a rural health system to offer essential, affordable, and high-quality services to rural populations? How might this policy result in disparate outcomes and widen health inequities, such as threatening access, slowing quality improvement, or creating financial barriers to obtaining health insurance or buying health care services?

The rural-proofing framework presented in this paper is a policy analysis tool for thinking about what the unintended consequences of a policy may be on rural populations and places vis-à-vis the objectives of a high-performance rural health system. Policy analysis must be applied to all sources of authoritative actions given that policies are produced not just in the legislative context, but also through judicial, administrative, and rulemaking actions.

The full report can be downloaded below.  

Slides from presentation by Priyanka Surio, Director, Data Analytics & Public Health Informatics, Association of State and Territorial Health Officials (ASTHO) at eHI's 12.4.18 Executive Advisory Board on Data Governance roundtable meeting.

Presentation slides and recording from 10.25.18 webinar.

As the healthcare industry moves into a more value-based market, consumer engagement is becoming an important factor. Patients who are more active in their own care, health, and well-being have lower healthcare costs and better health outcomes. In holistic, population focused-models, understanding and anticipating the needs of consumers is critical for patient engagement.

Combining a customer relationship management (CRM) system with a big data platform enhances consumer engagement and provides a clear picture about what affects patient care and health. These systems go beyond marketing and sales to track engagement, demographics, clinical status, behavior patterns, and preferences.

This webinar highlights the benefits of building a clinically-informed CRM solution. Speakers will share best practices on leveraging CRM technology to:

• Increase engagement and loyalty with enhanced customer experiences
• Close gaps in care and improve clinical and financial outcomes, with automated and proactive outreach
• Construct creative business efficiencies using closed-loop, integrated system

The use of Electronic Health Records to Support Population Health: A Systematic Review of the Literature

Electronic health records (EHRs) have emerged among health information technology as "meaningful use" to improve the quality and efficiency of healthcare, and health disparities in population health. In other instances, they have also shown lack of interoperability, functionality and many medical errors. With proper implementation and training, are electronic health records a viable source in managing population health? The primary objective of this systematic review is to assess the relationship of electronic health records’ use on population health through the identification and analysis of facilitators and barriers to its adoption for this purpose. Authors searched Cumulative Index of Nursing and Allied Health Literature (CINAHL) and MEDLINE (PubMed), 10/02/2012–10/02/2017, core clinical/academic journals, MEDLINE full text, English only, human species and evaluated the articles that were germane to our research objective. Each article was analyzed by multiple reviewers. Group members recognized common facilitators and barriers associated with EHRs effect on population health. A final list of articles was selected by the group after three consensus meetings (n = 55). Among a total of 26 factors identified, 63% (147/232) of those were facilitators and 37% (85/232) barriers. About 70% of the facilitators consisted of productivity/efficiency in EHRs occurring 33 times, increased quality and data management each occurring 19 times, surveillance occurring 17 times, and preventative care occurring 15 times. About 70% of the barriers consisted of missing data occurring 24 times, no standards (interoperability) occurring 13 times, productivity loss occurring 12 times, and technology too complex occurring 10 times. The analysis identified more facilitators than barriers to the use of the EHR to support public health. Wider adoption of the EHR and more comprehensive standards for interoperability will only enhance the ability for the EHR to support this important area of surveillance and disease prevention. This review identifies more facilitators than barriers to using the EHR to support public health, which implies a certain level of usability and acceptance to use the EHR in this manner. The public-health industry should combine their efforts with the interoperability projects to make the EHR both fully adopted and fully interoperable. This will greatly increase the availability, accuracy, and comprehensiveness of data across the country, which will enhance benchmarking and disease surveillance/prevention capabilities.

The full article can be downloaded below.  

Enhancing Rural Population Health Care Access and Outcomes Through the Telehealth EcoSystem™ Model

The article highlights the Telehealth EcoSystem™ model, a holistic cross-sector approach for socioeconomic revitalization, connectivity, interoperability and technology infrastructure development to address health equity for rural underserved communities. Two guiding frameworks, Community & Economic Development (CED) and Collective Impact, provided the foundation for the Telehealth EcoSystem™ model. Public and private organizational capacities are addressed by comprehensive healthcare and social service delivery through stakeholder engagement and collaborative decision-making processes. A focus is maintained on economic recovery and policy reforms that enhance population health outcomes for individuals and families who have economic challenges. The Telehealth EcoSystem™ utilizes an intranet mechanism that enables a range of technologies and electronic devices for health informatics and telemedicine initiatives. The relevance of the intranet to the advancement of health informatics is highlighted. Best practices in digital connectivity, HIPAA requirements, electronic health records (EHRs), and eHealth applications, such as patient portals and mobile devices, are emphasized. Collateral considerations include technology applications that expand public health services. The ongoing collaboration between a social science research corporation, a regional community foundation and an open access telecommunications carrier is a pivotal element in the sequential development and implementation of the Telehealth EcoSystem™ model in the rural southeastern region community.

The full article can be viewed below.  

Minimizing inequality in access to precision medicine in breast cancer by real-time population-based molecular analysis in the SCAN-B initiative

Selection of systemic therapy for primary breast cancer is currently based on clinical biomarkers along with stage. Novel genomic tests are continuously being introduced as more precise tools for guidance of therapy, although they are often developed for specific patient subgroups. The Sweden Cancerome Analysis Network – Breast (SCAN‐B) initiative aims to include all patients with breast cancer for tumour genomic analysis, and to deliver molecular subtype and mutational data back to the treating physician. An infrastructure for collection of blood and fresh tumour tissue from all patients newly diagnosed with breast cancer was set up in 2010, initially including seven hospitals within the southern Sweden regional catchment area, which has 1.8 million inhabitants. Inclusion of patients was implemented into routine clinical care, with collection of tumour tissue at local pathology departments for transport to the central laboratory, where routines for rapid sample processing, RNA sequencing and biomarker reporting were developed. More than 10 000 patients from nine hospitals have currently consented to inclusion in SCAN‐B with high (90 per cent) inclusion rates from both university and secondary hospitals. Tumour samples and successful RNA sequencing are being obtained from more than 70 per cent of patients, showing excellent representation compared with the national quality registry as a truly population‐based cohort. Molecular biomarker reports can be delivered to multidisciplinary conferences within 1 week. Population‐based collection of fresh tumour tissue is feasible given a decisive joint effort between academia and collaborative healthcare groups, and with governmental support. An infrastructure for genomic analysis and prompt data output paves the way for novel systemic therapy for patients from all hospitals, irrespective of size and location.

The full article can be viewed below.