An informatics approach to medication adherence assessment and improvement using clinical, billing, and patient-entered data

A novel hyperparameter search approach for accuracy and simplicity in disease prediction risk scoring

OBJECTIVE

Develop a novel technique to identify an optimal number of regression units corresponding to a single risk point, while creating risk scoring systems from logistic regression-based disease predictive models. The optimal value of this hyperparameter balances simplicity and accuracy, yielding risk scores of small scale and high accuracy for patient risk stratification.

MATERIALS AND METHODS

The proposed technique applies an adapted line search across all potential hyperparameter values. Additionally, DeLong test is integrated to ensure the selected value produces an accuracy insignificantly different from the best achievable risk score accuracy. We assessed the approach through two case studies predicting diabetic retinopathy (DR) within six months and hip fracture readmissions (HFR) within 30 days, involving cohorts of 90 400 diabetic patients and 18 065 hip fracture patients.

RESULTS

Our scores achieve accuracies insignificantly different from those obtained by existing approaches, reaching AUROCs of 0.803 and 0.645 for DR and HFR predictions, respectively. Regarding the scale, our scores ranged 0-53 for DR and 0-15 for HFR, while scores produced by existing methods frequently spanned hundreds or thousands.

DISCUSSION

According to the assessment, our risk scores offer simple and accurate predictions for diseases. Furthermore, our new DR score provides a competitive alternative to state-of-the-art risk scores for DR, while our HFR case study presents the first risk score for this condition.

CONCLUSION

Our technique offers a generalizable framework for crafting precise risk scores of compact scales, addressing the demand for user-friendly and effective risk stratification tool in healthcare.

Identifying the underlying factors associated with antidepressant drug discontinuation: content analysis of patients' drug reviews

The rate of antidepressant prescriptions is globally increasing. A large portion of patients stop their medications, which could lead to many side effects including relapse, and anxiety. The aim of this was to develop a drug-continuity prediction model and identify the factors associated with drug-continuity using online patient forums. We retrieved 982 antidepressant drug reviews from the online patient's forum AskaPatient.com. We followed the Analytical Framework Method to extract structured data from unstructured data. Using the structured data, we examined the factors associated with antidepressant discontinuity and developed a predictive model using multiple machine learning techniques. We tested multiple machine learning techniques which resulted in different performances ranging from accuracy of 65% to 82%. We found that Random Forest algorithm provides the highest prediction method with 82% Accuracy, 78% Precision, 88.03% Recall, and 84.2% F1-Score. The factors associated with drug discontinuity the most were: withdrawal symptoms, effectiveness-ineffectiveness, perceived-distress-adverse drug reaction, rating, and perceiveddistress related to withdrawal symptoms. Although the nature of data available at online forums differ from data collected through surveys, we found that online patients forum can be a valuable source of data for drug continuity prediction and understanding patients experience. The factors identified through our techniques were consistent with the findings of prior studies that used surveys.

Development of an electronic tool (e-AdPharm) to address unmet needs and barriers of community pharmacists to provide medication adherence support to patients

BACKGROUND

Community pharmacists are best placed to improve medication adherence because they frequently interact with patients and have been trained to manage medication-related problems. Therefore, it is essential to equip pharmacists adequately to detect non-adherent patients quickly and intervene to improve medication adherence.

OBJECTIVE

To design e-AdPharm, a tool that addresses unmet needs and barriers of community pharmacists to provide medication adherence support to patients with chronic diseases.

METHODS

A qualitative study using 4 focus group discussions with community pharmacists was conducted with a semi-structured interview guide and discussions lasting for 1-2 h. The discussions covered the barriers and needs of pharmacists related to medication adherence support provided to patients, their expectations of an electronic tool based on prescription refills to help them provide this support, and the design of the tool. Focus group data were coded and analyzed using an iterative process, with thematic and descriptive analyses.

RESULTS

Twenty-six community pharmacists participated. Lack of time and motivation from pharmacists and patients were common barriers to the provision of medication adherence support. Accordingly, community pharmacists wished to measure medication adherence quickly, provide easily interpretable data to patients on their medication use, and raise the patient's awareness of non-adherence. The pharmacists expressed their need to have an electronic tool to share medication adherence information with the treating physician. Regarding the design of e-AdPharm, the pharmacists wanted a table displaying medication adherence with a color code representing adherence level. They also stressed the importance of a structured section enabling them to continuously document the interventions made and the need for patient follow-ups.

CONCLUSIONS

e-AdPharm meet the needs and overcome the barriers of community pharmacists to provide medication adherence support to their patients. Future studies should examine the feasibility of implementing e-AdPharm in community pharmacies and test its efficacy for improving medication adherence.

Improving Health Care Management in Hospitals Through a Productivity Dashboard

Health information systems have been developed to help hospital managers steer daily operations, including key performance indicators (KPIs) for monitoring on a time-aggregated basis. Yet, current literature lacks in proposals of productivity dashboards to assist hospitals stakeholders. This research focuses on two related problems: (1) hospital organizations need access to productivity information to improve access to services; and (2) managers need productivity information to optimize resource allocation. This research consists in the development of dashboards to monitor information obtained from a hospital organization to support decision makers. To develop and evaluate the productivity dashboard, the Design Science Research (DSR) methodology was adopted. The dashboard was evaluated by stakeholders of a large Portuguese hospital who contributed to iteratively improving its design toward a useful decision support tool. Additionally, it was ascertained that monitoring productivity needs more study and that the dashboards on these themes are valuable assets at a monitoring level and subsequent decision-making process.

Outcomes research on children with medical complexity: A scoping review of gaps and opportunities

BACKGROUND

There has been a recent, rapid increase in the number of studies of children with medical complexity (CMC) and their families. There is a need for attention to gaps and patterns in this emerging field of study.

OBJECTIVES

The purpose of this scoping review was to identify patterns and gaps in the evidence related to classification systems, data, and outcomes in studies of CMC.

DATA SOURCES

We searched peer-reviewed journals for reports of quantitative studies focused on CMC outcomes published between 2008 and 2018. On the basis of a structured screening process, we selected 63 reports that met our inclusion criteria.

STUDY APPRAISAL AND SYNTHESIS

We used the methodological framework for scoping studies described by Arskey and O'Malley to map relevant literature in the field and the ECHO model to categorize studies according to three health outcome domains (economic, clinical, and humanistic).

RESULTS

The terminology used to describe and classify CMC differed across studies depending on outcome domain. Two thirds of the reports focused on economic outcomes; fewer than a quarter included child or family quality of life as an outcome. A majority of studies used a single source of data, with robust analyses of administrative, payer, and publicly available data.

CONCLUSIONS AND IMPLICATIONS OF KEY FINDINGS

Research on CMC and their families would benefit from standardization of terms and classification systems, the use of measurement strategies that map humanistic outcomes as trajectories, and more attention to outcomes identified as most meaningful to CMC and their families.

Development of an algorithm to link electronic health record prescriptions with pharmacy dispense claims

Objective

Medication adherence is an important aspect of chronic disease management. Electronic health record (EHR) data are often not linked to dispensing data, limiting clinicians' understanding of which of their patients fill their medications, and how to tailor care appropriately. We aimed to develop an algorithm to link EHR prescribing to claims-based dispensing data and use the results to quantify how often patients with diabetes filled prescribed chronic disease medications.

Materials and Methods

We developed an algorithm linking EHR prescribing data (RxNorm terminology) to claims-based dispensing data (NDC terminology), within sample of adult (19-64) community health center (CHC) patients with diabetes from a network of CHCs across 12 states. We demonstrate an application of the method by calculating dispense rates for a set of commonly prescribed diabetes and cardio-protective medications. To further inform clinical care, we computed adjusted odds ratios of dispense by patient-, encounter-, and clinic-level characteristics.

Results

Seventy-six percent of cardio-protective medication prescriptions and 74% of diabetes medications were linked to a dispensing record. Age, income, ethnicity, insurance, assigned primary care provider, comorbidity, time on EHR, and clinic size were significantly associated with odds of dispensing.

Discussion

EHR prescriptions and pharmacy dispense data can be linked at the record level across different terminologies. Dispensing rates in this low-income population with diabetes were similar to other populations.

Conclusion

Record linkage resulted in the finding that CHC patients with diabetes largely had their chronic disease medications dispensed. Understanding factors associated with dispensing rates highlight barriers and opportunities for optimal disease management.

A dashboard-based system for supporting diabetes care

Objective

To describe the development, as part of the European Union MOSAIC (Models and Simulation Techniques for Discovering Diabetes Influence Factors) project, of a dashboard-based system for the management of type 2 diabetes and assess its impact on clinical practice.

Methods

The MOSAIC dashboard system is based on predictive modeling, longitudinal data analytics, and the reuse and integration of data from hospitals and public health repositories. Data are merged into an i2b2 data warehouse, which feeds a set of advanced temporal analytic models, including temporal abstractions, care-flow mining, drug exposure pattern detection, and risk-prediction models for type 2 diabetes complications. The dashboard has 2 components, designed for (1) clinical decision support during follow-up consultations and (2) outcome assessment on populations of interest. To assess the impact of the clinical decision support component, a pre-post study was conducted considering visit duration, number of screening examinations, and lifestyle interventions. A pilot sample of 700 Italian patients was investigated. Judgments on the outcome assessment component were obtained via focus groups with clinicians and health care managers.

Results

The use of the decision support component in clinical activities produced a reduction in visit duration (P ≪ .01) and an increase in the number of screening exams for complications (P < .01). We also observed a relevant, although nonstatistically significant, increase in the proportion of patients receiving lifestyle interventions (from 69% to 77%). Regarding the outcome assessment component, focus groups highlighted the system's capability of identifying and understanding the characteristics of patient subgroups treated at the center.

Conclusion

Our study demonstrates that decision support tools based on the integration of multiple-source data and visual and predictive analytics do improve the management of a chronic disease such as type 2 diabetes by enacting a successful implementation of the learning health care system cycle.

Providing physicians with feedback on medication adherence for people with chronic diseases taking long-term medication

BACKGROUND

Poor medication adherence decreases treatment efficacy and worsens clinical outcomes, but average rates of adherence to long-term pharmacological treatments for chronic illnesses are only about 50%. Interventions for improving medication adherence largely focus on patients rather than on physicians; however, the strategies shown to be effective are complex and difficult to implement in clinical practice. There is a need for new care models addressing the problem of medication adherence, integrating this problem into the patient care process. Physicians tend to overestimate how well patients take their medication as prescribed. This can lead to missed opportunities to change medications, solve adverse effects, or propose the use of reminders in order to improve patients' adherence. Thus, providing physicians with feedback on medication adherence has the potential to prompt changes that improve their patients' adherence to prescribed medications.

OBJECTIVES

To assess the effects of providing physicians with feedback about their patients' medication adherence for improving adherence. We also assessed the effects of the intervention on patient outcomes, health resource use, and processes of care.

SEARCH METHODS

We conducted a systematic search of the Cochrane Central Register of Controlled Trials (CENTRAL), MEDLINE, and Embase, all from database inception to December 2016 and without any language restriction. We also searched ISI Web of Science, two trials registers, and grey literature.

SELECTION CRITERIA

We included randomised trials, controlled before-after studies, and interrupted time series studies that compared the effects of providing feedback to physicians about their patients' adherence to prescribed long-term medications for chronic diseases versus usual care. We included published or unpublished studies in any language. Participants included any physician and any patient prescribed with long-term medication for chronic disease. We included interventions providing the prescribing physician with information about patient adherence to medication. Only studies in which feedback to the physician was the sole intervention or the essential component of a multifaceted intervention were eligible. In the comparison groups, the physicians should not have had access to information about their patients' adherence to medication. We considered the following outcomes: medication adherence, patient outcomes, health resource use, processes of care, and adverse events.

DATA COLLECTION AND ANALYSIS

Two independent review authors extracted and analysed all data using standard methodological procedures expected by Cochrane and the Effective Practice and Organisation of Care group. Due to heterogeneity in study methodology, comparison groups, intervention settings, and measurements of outcomes, we did not carry out meta-analysis. We describe the impact of interventions on outcomes in tabular form and make a qualitative assessment of the effects of studies.

MAIN RESULTS

We included nine studies (23,255 patient participants): eight randomised trials and one interrupted time series analysis. The studies took place in primary care and other outpatient settings in the USA and Canada. Seven interventions involved the systematic provision of feedback to physicians concerning all their patients' adherence to medication, and two interventions involved issuing an alert for non-adherent patients only. Seven studies used pharmacy refill data to assess medication adherence, and two used an electronic device or self-reporting. The definition of adherence differed across studies, making comparisons difficult. Eight studies were at high risk of bias, and one study was at unclear risk of bias. The most frequent source of bias was lack of protection against contamination.Providing physicians with feedback may lead to little or no difference in medication adherence (seven studies, 22,924 patients), patient outcomes (two studies, 1292 patients), or health resource use (two studies, 4181 patients). Providing physicians with feedback on medication adherence may improve processes of care (e.g. more medication changes, dialogue with patient, management of uncontrolled hypertension) compared to usual care (four studies, 2780 patients). None of the studies reported an adverse event due to the intervention. The certainty of evidence was low for all outcomes, mainly due to high risk of bias, high heterogeneity across studies, and indirectness of evidence.

AUTHORS' CONCLUSIONS

Across nine studies, we observed little or no evidence that provision of feedback to physicians regarding their patients adherence to prescribed medication improved medication adherence, patient outcomes, or health resource use. Feedback about medication adherence may improve processes of care, but due to the small number of studies assessing this outcome and high risk of bias, we cannot draw firm conclusions on the effect of feedback on this outcome. Future research should use a clear, standardised definition of medication adherence and cluster-randomisation to avoid the risk of contamination.

Patient Health Record Systems Scope and Functionalities: Literature Review and Future Directions

BACKGROUND

A new generation of user-centric information systems is emerging in health care as patient health record (PHR) systems. These systems create a platform supporting the new vision of health services that empowers patients and enables patient-provider communication, with the goal of improving health outcomes and reducing costs. This evolution has generated new sets of data and capabilities, providing opportunities and challenges at the user, system, and industry levels.

OBJECTIVE

The objective of our study was to assess PHR data types and functionalities through a review of the literature to inform the health care informatics community, and to provide recommendations for PHR design, research, and practice.

METHODS

We conducted a review of the literature to assess PHR data types and functionalities. We searched PubMed, Embase, and MEDLINE databases from 1966 to 2015 for studies of PHRs, resulting in 1822 articles, from which we selected a total of 106 articles for a detailed review of PHR data content.

RESULTS

We present several key findings related to the scope and functionalities in PHR systems. We also present a functional taxonomy and chronological analysis of PHR data types and functionalities, to improve understanding and provide insights for future directions. Functional taxonomy analysis of the extracted data revealed the presence of new PHR data sources such as tracking devices and data types such as time-series data. Chronological data analysis showed an evolution of PHR system functionalities over time, from simple data access to data modification and, more recently, automated assessment, prediction, and recommendation.

CONCLUSIONS

Efforts are needed to improve (1) PHR data quality through patient-centered user interface design and standardized patient-generated data guidelines, (2) data integrity through consolidation of various types and sources, (3) PHR functionality through application of new data analytics methods, and (4) metrics to evaluate clinical outcomes associated with automated PHR system use, and costs associated with PHR data storage and analytics.

Personalization and Patient Involvement in Decision Support Systems: Current Trends

OBJECTIVES

This survey aims at highlighting the latest trends (2012-2014) on the development, use, and evaluation of Information and Communication Technologies (ICT) based decision support systems (DSSs) in medicine, with a particular focus on patient-centered and personalized care.

METHODS

We considered papers published on scientific journals, by querying PubMed and Web of ScienceTM. Included studies focused on the implementation or evaluation of ICT-based tools used in clinical practice. A separate search was performed on computerized physician order entry systems (CPOEs), since they are increasingly embedding patient-tailored decision support.

RESULTS

We found 73 papers on DSSs (53 on specific ICT tools) and 72 papers on CPOEs. Although decision support through the delivery of recommendations is frequent (28/53 papers), our review highlighted also DSSs only based on efficient information presentation (25/53). Patient participation in making decisions is still limited (9/53), and mostly focused on risk communication. The most represented medical area is cancer (12%). Policy makers are beginning to be included among stakeholders (6/73), but integration with hospital information systems is still low. Concerning knowledge representation/management issues, we identified a trend towards building inference engines on top of standard data models. Most of the tools (57%) underwent a formal assessment study, even if half of them aimed at evaluating usability and not effectiveness.

CONCLUSIONS

Overall, we have noticed interesting evolutions of medical DSSs to improve communication with the patient, consider the economic and organizational impact, and use standard models for knowledge representation. However, systems focusing on patient-centered care still do not seem to be available at large.

Computation of adherence to medication and visualization of medication histories in R with AdhereR: Towards transparent and reproducible use of electronic healthcare data

Adherence to medications is an important indicator of the quality of medication management and impacts on health outcomes and cost-effectiveness of healthcare delivery. Electronic healthcare data (EHD) are increasingly used to estimate adherence in research and clinical practice, yet standardization and transparency of data processing are still a concern. Comprehensive and flexible open-source algorithms can facilitate the development of high-quality, consistent, and reproducible evidence in this field. Some EHD-based clinical decision support systems (CDSS) include visualization of medication histories, but this is rarely integrated in adherence analyses and not easily accessible for data exploration or implementation in new clinical settings. We introduce AdhereR, a package for the widely used open-source statistical environment R, designed to support researchers in computing EHD-based adherence estimates and in visualizing individual medication histories and adherence patterns. AdhereR implements a set of functions that are consistent with current adherence guidelines, definitions and operationalizations. We illustrate the use of AdhereR with an example dataset of 2-year records of 100 patients and describe the various analysis choices possible and how they can be adapted to different health conditions and types of medications. The package is freely available for use and its implementation facilitates the integration of medication history visualizations in open-source CDSS platforms.

Measuring Practicing Clinicians' Information Literacy. An Exploratory Analysis in the Context of Panel Management

BACKGROUND

As healthcare moves towards technology-driven population health management, clinicians must adopt complex digital platforms to access health information and document care.

OBJECTIVES

This study explored information literacy, a set of skills required to effectively navigate population health information systems, among primary care providers in one Veterans' Affairs (VA) medical center.

METHODS

Information literacy was assessed during an 8-month randomized trial that tested a population health (panel) management intervention. Providers were asked about their use and comfort with two VA digital tools for panel management at baseline, 16 weeks, and post-intervention. An 8-item scale (range 0-40) was used to measure information literacy (Cronbach's α=0.84). Scores between study arms and provider types were compared using paired t-tests and ANOVAs. Associations between self-reported digital tool use and information literacy were measured via Pearson's correlations.

RESULTS

Providers showed moderate levels of information literacy (M= 27.4, SD 6.5). There were no significant differences in mean information literacy between physicians (M=26.4, SD 6.7) and nurses (M=30.5, SD 5.2, p=0.57 for difference), or between intervention (M=28.4, SD 6.5) and control groups (M=25.1, SD 6.2, p=0.12 for difference). Information literacy was correlated with higher rates of self-reported information system usage (r=0.547, p=0.001). Clinicians identified data access, accuracy, and interpretability as potential information literacy barriers.

CONCLUSIONS

While exploratory in nature, cautioning generalizability, the study suggests that measuring and improving clinicians' information literacy may play a significant role in the implementation and use of digital information tools, as these tools are rapidly being deployed to enhance communication among care teams, improve health care outcomes, and reduce overall costs.

Patient-Centered Tablet Application for Improving Medication Adherence after a Drug-Eluting Stent

BACKGROUND/AIMS

This study's objective was to evaluate a patient-centered educational electronic tablet application, "My Interventional Drug-Eluting Stent Educational App" (MyIDEA) to see if there was an increase in patient knowledge about dual antiplatelet therapy (DAPT) and medication possession ratio (MPR) compared to treatment as usual.

METHODS

In a pilot project, 24 elderly (≥50 years old) research participants were recruited after a drug-eluting stent. Eleven were randomized to the control arm and 13 to the interventional arm. All the participants completed psychological and knowledge questionnaires. Adherence was assessed through MPR, which was calculated at 3 months for all participants who were scheduled for second and third follow-up visits.

RESULTS

Relative to control, the interventional group had a 10% average increase in MPR. As compared to the interventional group, more patients in the control group had poor adherence (<80% MPR). The psychological data revealed a single imbalance in anxiety between the control and interventional groups. On average, interventional participants spent 21 min using MyIDEA.

DISCUSSION

Consumer health informatics has enabled us to engage patients with their health data using novel methods. Consumer health technology needs to focus more on patient knowledge and engagement to improve long-term health. MyIDEA takes a unique approach in targeting DAPT from the onset.

CONCLUSION

MyIDEA leverages patient-centered information with clinical care and the electronic health record highlighting the patients' role as a team member in their own health care. The patients think critically about adverse events and how to solve issues before leaving the hospital.

Development of a cognitive framework of patient record summary review in the formative phase of user-centered design

Excellent usability characteristics allow electronic health record (EHR) systems to more effectively support clinicians providing care and contribute to better quality and safety. The Office of the National Coordinator for Health IT (ONC) therefore requires all vendors to follow a User-Centered Design (UCD) process to increase the usability of their products in order to meet certification criteria for the Safety-Enhanced Design part of the Meaningful Use (stage 2) EHR incentive program. This report describes the initial stage of a UCD process in which foundational design concepts were formulated. We designed a functional prototype of an EHR module intended to help clinicians to efficiently complete a summary review of an electronic patient record before an ambulatory visit. Cognitively-based studies were performed and the results used to develop a cognitive framework that subsequently guided design of a prototype. Results showed that clinicians categorized and reasoned with patient data in distinct patterns; they preferred to review relevant history in the assessment and plan section of the most recent note, to search for changes in health and for new episodes of care since the last visit and to look up current-day data such as vital signs. These basic concepts were represented in the design, for instance, by screen division into vertical thirds that had historical content to the left and most recent data to the right. Other characteristics such as visual association of contextual information or direct, one-click access to the assessment and plan section of visit notes were directly informed by our findings and refined in a series of UCD-specific iterative testing. Understanding of tasks and cognitive demands early in the UCD process was critically important for developing a tool optimized for reasoning and workflow preferences of clinicians.

Integration of Provider, Pharmacy, and Patient-Reported Data to Improve Medication Adherence for Type 2 Diabetes: A Controlled Before-After Pilot Study

Background

Patients with diabetes often have poor adherence to using medications as prescribed. The reasons why, however, are not well understood. Furthermore, most health care delivery processes do not routinely assess medication adherence or the factors that contribute to poor adherence.

Objective

The objective of the study was to assess the feasibility of an integrated informatics approach to aggregating and displaying clinically relevant data with the potential to identify issues that may interfere with appropriate medication utilization and facilitate patient-provider communication during clinical encounters about strategies to improve medication use.

Methods

We developed a clinical dashboard within an electronic health record (EHR) system that uses data from three sources: the medical record, pharmacy claims, and a patient portal. Next, we implemented the dashboard into three community health centers. Health care providers (n=15) and patients with diabetes (n=96) were enrolled in a before-after pilot to test the system’s impact on medication adherence and clinical outcomes. To measure adherence, we calculated the proportion of days covered using pharmacy claims. Demographic, laboratory, and visit data from the EHR were analyzed using pairwise t tests. Perceived barriers to adherence were self-reported by patients. Providers were surveyed about their use and perceptions of the clinical dashboard.

Results

Adherence significantly and meaningfully improved (improvements ranged from 6%-20%) consistently across diabetes as well as cardiovascular drug classes. Clinical outcomes, including HbA1c, blood pressure, lipid control, and emergency department utilization remained unchanged. Only a quarter of patients (n=24) logged into the patient portal and completed psychosocial questionnaires about their barriers to taking medications.

Conclusions

Integrated approaches using advanced EHR, clinical decision support, and patient-controlled technologies show promise for improving appropriate medication use and supporting better management of chronic conditions. Future research and development is necessary to design, implement, and integrate the myriad of EHR and clinical decision support systems as well as patient-focused information systems into routine care and patient processes that together support health and well-being.

Template for preparation of papers for IEEE sponsored conferences & symposia

To improve the access to medical information is necessary to design and implement integrated informatics techniques aimed to gather data from different and heterogeneous sources. This paper describes the technologies used to integrate data coming from the electronic medical record of the IRCCS Fondazione Maugeri (FSM) hospital of Pavia, Italy, and combines them with administrative, pharmacy drugs purchase coming from the local healthcare agency (ASL) of the Pavia area and environmental open data of the same region. The integration process is focused on data coming from a cohort of one thousand patients diagnosed with Type 2 Diabetes Mellitus (T2DM). Data analysis and temporal data mining techniques have been integrated to enhance the initial dataset allowing the possibility to stratify patients using further information coming from the mined data like behavioral patterns of prescription-related drug purchases and other frequent clinical temporal patterns, through the use of an intuitive dashboard controlled system.

Utilizing an integrated infrastructure for outcomes research: a systematic review

OBJECTIVE

To explore the ability of an integrated health information infrastructure to support outcomes research.

METHODS

A systematic review of articles published from 1983 to 2012 by Regenstrief Institute investigators using data from an integrated electronic health record infrastructure involving multiple provider organisations was performed. Articles were independently assessed and classified by study design, disease and other metadata including bibliometrics.

RESULTS

A total of 190 articles were identified. Diseases included cognitive, (16) cardiovascular, (16) infectious, (15) chronic illness (14) and cancer (12). Publications grew steadily (26 in the first decade vs. 100 in the last) as did the number of investigators (from 15 in 1983 to 62 in 2012). The proportion of articles involving non-Regenstrief authors also expanded from 54% in the first decade to 72% in the last decade. During this period, the infrastructure grew from a single health system into a health information exchange network covering more than 6 million patients. Analysis of journal and article metrics reveals high impact for clinical trials and comparative effectiveness research studies that utilised data available in the integrated infrastructure.

DISCUSSION

Integrated information infrastructures support growth in high quality observational studies and diverse collaboration consistent with the goals for the learning health system. More recent publications demonstrate growing external collaborations facilitated by greater access to the infrastructure and improved opportunities to study broader disease and health outcomes.

CONCLUSIONS

Integrated information infrastructures can stimulate learning from electronic data captured during routine clinical care but require time and collaboration to reach full potential.

Does Informatics Enable or Inhibit the Delivery of Patient-centred, Coordinated, and Quality-assured Care: a Delphi Study. A Contribution of the IMIA Primary Health Care Informatics Working Group

BACKGROUND

Primary care delivers patient-centred and coordinated care, which should be quality-assured. Much of family practice now routinely uses computerised medical record (CMR) systems, these systems being linked at varying levels to laboratories and other care providers. CMR systems have the potential to support care.

OBJECTIVE

To achieve a consensus among an international panel of health care professionals and informatics experts about the role of informatics in the delivery of patient-centred, coordinated, and quality-assured care.

METHOD

The consensus building exercise involved 20 individuals, five general practitioners and 15 informatics academics, members of the International Medical Informatics Association Primary Care Informatics Working Group. A thematic analysis of the literature was carried out according to the defined themes.

RESULTS

The first round of the analysis developed 27 statements on how the CMR, or any other information system, including paper-based medical records, supports care delivery. Round 2 aimed at achieving a consensus about the statements of round one. Round 3 stated that there was an agreement on informatics principles and structures that should be put in place. However, there was a disagreement about the processes involved in the implementation, and about the clinical interaction with the systems after the implementation.

CONCLUSIONS

The panel had a strong agreement about the core concepts and structures that should be put in place to support high quality care. However, this agreement evaporated over statements related to implementation. These findings reflect literature and personal experiences: whilst there is consensus about how informatics structures and processes support good quality care, implementation is difficult.

Big Data Technologies: New Opportunities for Diabetes Management

The so-called big data revolution provides substantial opportunities to diabetes management. At least 3 important directions are currently of great interest. First, the integration of different sources of information, from primary and secondary care to administrative information, may allow depicting a novel view of patient's care processes and of single patient's behaviors, taking into account the multifaceted nature of chronic care. Second, the availability of novel diabetes technologies, able to gather large amounts of real-time data, requires the implementation of distributed platforms for data analysis and decision support. Finally, the inclusion of geographical and environmental information into such complex IT systems may further increase the capability of interpreting the data gathered and extract new knowledge from them. This article reviews the main concepts and definitions related to big data, it presents some efforts in health care, and discusses the potential role of big data in diabetes care. Finally, as an example, it describes the research efforts carried on in the MOSAIC project, funded by the European Commission.