The face of Canadian medicare is going to look much different following this federal budget

The federal government has attempted to downplay the effect its recent budget will have on health care, but observers ranging from provincial governments to health care organizations say there will be a huge impact. Premier Bob Rae said that from 1996 to 1998 Ontario's loss will total $3.6 billion for health care, education and social services, an amount that almost equals the combined total annual expenditures for its drug-benefit program, nearly all cancer treatment in the province and the cost of running almost all 20 hospitals in the city of Toronto. Dr. Léo-Paul Landry, the CMA secretary general, says the budget “is probably the beginning of the end of medicare as we know it.”     

“Not all my friends need to know”: a qualitative study of teenage patients,privacy, and social media

Background

The literature describes teenagers as active users of social media, who seem to care about privacy, but who also reveal a considerable amount of personal information. There have been no studies of how they manage personal health information on social media.

Objective

To understand how chronically ill teenage patients manage their privacy on social media sites.

Design

A qualitative study based on a content analysis of semistructured interviews with 20 hospital patients (12–18 years).

Results

Most teenage patients do not disclose their personal health information on social media, even though the study found a pervasive use of Facebook. Facebook is a place to be a “regular”, rather than a sick teenager. It is a place where teenage patients stay up-to-date about their social life—it is not seen as a place to discuss their diagnosis and treatment. The majority of teenage patients don''t use social media to come into contact with others with similar conditions and they don''t use the internet to find health information about their diagnosis.

Conclusions

Social media play an important role in the social life of teenage patients. They enable young patients to be “regular” teenagers. Teenage patients'' online privacy behavior is an expression of their need for self-definition and self-protection.     

A systems approach to accepted standards of care: Shifting the blame

In healthcare, from a legal perspective, the standard of acceptable practice has been generally set by the courts and defined as healthcare professionals acting in a manner that is widely accepted by their peers as meeting an acceptable standard of care. This view, however, reflects the state of how practice “is” rather than what it “ought to be”. What is ought to be depends on whether you take a “person” or “system” oriented approach to practice.The increasing pressures of lack of money and resources, and an ever–increasing need for care are bringing pressure on the health services to move to a system approach and this is gaining acceptance both with clinicians and thus eventually the courts.A systems-type approach to healthcare will, by necessity, embrace clinical protocols and guidelines supported by clinical information systems. It will also see blame for errors shifting from clinicians to the organisations that employ them.This paper argues that a continued use of a person-based approach to healthcare, developed through an historical record of practice by individual clinicians, is no longer adequate defence in a case of supposed negligence.When the healthcare system has codified clinical guidelines and digital data gathered across thousands of clinicians and their patients, it is possible to compute adequate levels of care and expect clinicians and the healthcare system in general to meet these minimum standards.Future negligence decisions will rely on a systems-based best practice standard of care determined through evidence rather than opinion     

Breaching the Security of the Kaiser Permanente Internet Patient Portal: the Organizational Foundations of Information Security

This case study describes and analyzes a breach of the confidentiality and integrity of personally identified health information (e.g. appointment details, answers to patients’ questions, medical advice) for over 800 Kaiser Permanente (KP) members through KP Online, a web-enabled health care portal. The authors obtained and analyzed multiple types of qualitative data about this incident including interviews with KP staff, incident reports, root cause analyses, and media reports. Reasons at multiple levels account for the breach, including the architecture of the information system, the motivations of individual staff members, and differences among the subcultures of individual groups within as well as technical and social relations across the Kaiser IT program. None of these reasons could be classified, strictly speaking, as “security violations.” This case study, thus, suggests that, to protect sensitive patient information, health care organizations should build safe organizational contexts for complex health information systems in addition to complying with good information security practice and regulations such as the Health Insurance Portability and Accountability Act (HIPAA) of 1996.     

CMA brief offers suggestions as federal commission studies country's blood-gathering system

The CMA hopes the Commission of Inquity on the Blood System, chaired by Mr. Justice Horace Krever, “will restore faith in a system that is essential for the health and safety of all Canadians.” However, the cost implications any recommendations may have for the health care system must also be taken into consideration. The CMA made several recommendations in response to the commission's interim report.     

Leveraging health information technology to achieve the “triple aim” of healthcare reform

Objective To investigate experiences with leveraging health information technology (HIT) to improve patient care and population health, and reduce healthcare expenditures.Materials and methods In-depth qualitative interviews with federal government employees, health policy, HIT and medico-legal experts, health providers, physicians, purchasers, payers, patient advocates, and vendors from across the United States.Results The authors undertook 47 interviews. There was a widely shared belief that Health Information Technology for Economic and Clinical Health (HITECH) had catalyzed the creation of a digital infrastructure, which was being used in innovative ways to improve quality of care and curtail costs. There were however major concerns about the poor usability of electronic health records (EHRs), their limited ability to support multi-disciplinary care, and major difficulties with health information exchange, which undermined efforts to deliver integrated patient-centered care. Proposed strategies for enhancing the benefits of HIT included federal stimulation of competition by mandating vendors to open-up their application program interfaces, incenting development of low-cost consumer informatics tools, and promoting Congressional review of the The Health Insurance Portability and Accountability Act (HIPPA) to optimize the balance between data privacy and reuse. Many underscored the need to “kick the legs from underneath the fee-for-service model” and replace it with a data-driven reimbursement system that rewards high quality care.Conclusions The HITECH Act has stimulated unprecedented, multi-stakeholder interest in HIT. Early experiences indicate that the resulting digital infrastructure is being used to improve quality of care and curtail costs. Reform efforts are however severely limited by problems with usability, limited interoperability and the persistence of the fee-for-service paradigm—addressing these issues therefore needs to be the federal government’s main policy target.     

Computerized Physician Order Entry: Helpful or Harmful?

Computerized physician order entry (CPOE) is touted as a major improvement in patient safety, primarily as a result of the Institute of Medicine''s 1999 report on medical errors and the subsequent formation of the “Leapfrog Group” of companies to preferentially direct their employees'' health care to those institutions that install such systems (as part of directives that “Leapfrog” feels will improve patient care). Although the literature suggests that such systems have the potential to improve patient outcomes through decrease of adverse drug events, actual improvements in medical outcomes have not been documented. Installation of such systems could actually increase the number of adverse drug events and result in higher overall medical costs, particularly in the first few years of their adoption.In the last five years, hospitals, including our own, have begun to use computerized systems that require physicians and other health care providers to electronically enter patient care orders.¹ Before this time, only a handful of hospitals used such systems. These computer programs contain algorithms that alert health care providers to potentially harmful therapeutic decisions before orders are processed. The installation of these systems is costly (millions of dollars) and requires major behavioral changes, not only by physicians, but also by the entire health care organization.² In January 2003, Cedars-Sinai Health System in Los Angeles removed its recently installed computerized physician order entry (CPOE) system from use after almost unanimous protest from the medical staff. Why are hospitals and other health care organizations pursuing this avenue at this time? Does the literature support the premise that these systems are beneficial for patient care? Do such systems decrease total health care costs? The answers to these questions are still evolving. In this forum, we address these questions and describe some of the pertinent medical literature on this subject.     

Relative costs of specialist services in a family practice population

The frequency and cost of referrals to specialists in March 1984 for 8980 rostered patients attending a family practice clinic located in a teaching hospital were analysed. The patients made 1891 visits to specialists. In all age groups and for all specialties female patients were more likely to be seen. The total direct provider costs were higher for female patients than for male patients. However, costs per patient seen were higher for male patients, except for psychiatry and medicine. Visits to surgeons had the highest total cost, while visits to psychiatrists had the highest cost per patient seen. Of the direct provider costs 61% was for specialist services. The family physician, in the “gatekeeper” role, has an opportunity to control some of the costs of the health care system by ensuring that the best and most efficient use is made of the referral network.     

Informatics,evidence-based care,and research; implications for national policy: a report of an American Medical Informatics Association health policy conference

There is an increased level of activity in the biomedical and health informatics world (e-prescribing, electronic health records, personal health records) that, in the near future, will yield a wealth of available data that we can exploit meaningfully to strengthen knowledge building and evidence creation, and ultimately improve clinical and preventive care. The American Medical Informatics Association (AMIA) 2008 Health Policy Conference was convened to focus and propel discussions about informatics-enabled evidence-based care, clinical research, and knowledge management. Conference participants explored the potential of informatics tools and technologies to improve the evidence base on which providers and patients can draw to diagnose and treat health problems. The paper presents a model of an evidence continuum that is dynamic, collaborative, and powered by health informatics technologies. The conference''s findings are described, and recommendations on terminology harmonization, facilitation of the evidence continuum in a “wired” world, development and dissemination of clinical practice guidelines and other knowledge support strategies, and the role of diverse stakeholders in the generation and adoption of evidence are presented.     

Impact of event notification services on timely follow-up and rehospitalization among primary care patients at two Veterans Affairs Medical Centers

ObjectiveTo examine the effectiveness of event notification service (ENS) alerts on health care delivery processes and outcomes for older adults.Materials and methodsWe deployed ENS alerts in 2 Veterans Affairs (VA) medical centers using regional health information exchange (HIE) networks from March 2016 to December 2019. Alerts targeted VA-based primary care teams when older patients (aged 65+ years) were hospitalized or attended emergency departments (ED) outside the VA system. We employed a concurrent cohort study to compare postdischarge outcomes between patients whose providers received ENS alerts and those that did not (usual care). Outcome measures included: timely follow-up postdischarge (actual phone call within 7 days or an in-person primary care visit within 30 days) and all-cause inpatient or ED readmission within 30 days. Generalized linear mixed models, accounting for clustering by primary care team, were used to compare outcomes between groups.ResultsCompared to usual care, veterans whose primary care team received notification of non-VA acute care encounters were 4 times more likely to have phone contact within 7 days (AOR = 4.10, P < .001) and 2 times more likely to have an in-person visit within 30 days (AOR = 1.98, P = .007). There were no significant differences between groups in hospital or ED utilization within 30 days of index discharge (P = .057).DiscussionENS was associated with increased timely follow-up following non-VA acute care events, but there was no associated change in 30-day readmission rates. Optimization of ENS processes may be required to scale use and impact across health systems.ConclusionGiven the importance of ENS to the VA and other health systems, this study provides guidance for future research on ENS for improving care coordination and population outcomes.Trial RegistrationClinicalTrials.gov {"type":"clinical-trial","attrs":{"text":"NCT02689076","term_id":"NCT02689076"}}NCT02689076. “Regional Data Exchange to Improve Care for Veterans After Non-VA Hospitalization.” Registered February 23, 2016.     

Buffeted by harsh economic winds, medical schools turn a wary eye to the future

All parts of Canada's health care system are facing fiscal pressures these days, but they are particularly great at Canada's medical schools. However, Dr. David Hawkins of the Association of Canadian Medical Colleges is optimistic that all 16 of Canada's medical schools will remain open, mainly because of the huge impact they have on health care in their local communities. “We don't just turn out students — we raise the standard of health care in a whole community,” he says.     

Polls point to growing gap between MDs, patients over financing of health care

An unprecedented round of CMA polling points to some startling differences in the way physicians and patients perceive the financial problems facing Canada's health care system. One poll indicates that doctors consider recent federal budget cuts a sign that the private sector will soon have a major role to play in health care financing. However, a poll of nonphysicians shows that Canadians oppose any move toward “out-of-pocket” payments for health care services, including payments for private insurance. The CMA board has responded by turning much of the 1995 annual meeting over to the topic of the future of health care in Canada, including financing, and the medical profession's response.     

Three Decades of Research on Computer Applications in Health Care: Medical Informatics Support at the Agency for Healthcare Research and Quality

The Agency for Healthcare Research and Quality and its predecessor organizations—collectively referred to here as AHRQ—have a productive history of funding research and development in the field of medical informatics, with grant investments since 1968 totaling $107 million. Many computerized interventions that are commonplace today, such as drug interaction alerts, had their genesis in early AHRQ initiatives.This review provides a historical perspective on AHRQ investment in medical informatics research. It shows that grants provided by AHRQ resulted in achievements that include advancing automation in the clinical laboratory and radiology, assisting in technology development (computer languages, software, and hardware), evaluating the effectiveness of computer-based medical information systems, facilitating the evolution of computer-aided decision making, promoting computer-initiated quality assurance programs, backing the formation and application of comprehensive data banks, enhancing the management of specific conditions such as HIV infection, and supporting health data coding and standards initiatives.Other federal agencies and private organizations have also supported research in medical informatics, some earlier and to a greater degree than AHRQ. The results and relative roles of these related efforts are beyond the scope of this review.Three decades ago, when the federal government''s National Center for Health Services Research and Development began to support research on computer applications in health care, few imagined the impact that information systems and sciences would have on medical care today. For most, the idea of a national clearinghouse of guidelines, available through a computer that sits on a home office desktop, seemed like science fiction. For a few researchers and those supporting their work, however, visions of what could become possible in the management of health care information called for development of computerized systems and the evaluation of their effects on quality, cost, and access to care.The Agency for Healthcare Research and Quality (AHRQ, from 1999) and its predecessor agencies—the National Center for Health Services Research and Development (beginning in 1968) and the Agency for Health Care Policy and Research (from 1989 to 1999)—have a rich history of funding research, development, and evaluation in medical informatics. Although the grant investments since 1968 total only $107 million ($246 million in 2000 dollars), they supported initiatives that have established a research framework for many of the computer applications now being used today.The focus of AHRQ''s early research funding in medical informatics was on acquiring patient care data and communicating patient care management information. The goal was not only to improve the quality of care, but also to achieve reductions in costs and medical personnel resource use by processing data more efficiently. Research aimed at improving communication of information was targeted at what we would call today “getting the right information to the right place at the right time.” The promise of this research was its ability to provide findings that would guide reorganization of care delivery, take advantage of the more rapid communication of necessary information, and reduce manpower needs.¹ Over time, AHRQ''s funding has emphasized the application of health services research methods to evaluations of information technology used in community health settings. This article highlights key accomplishments emerging from AHRQ''s funding that have improved the quality of patient care in studied sites and have the potential to improve health care in all settings.Other federal agencies (such as the National Library of Medicine, the Veterans Health Administration, and the Department of Defense) and private organizations (such as The John A. Hartford Foundation, The Robert Wood Johnson Foundation, and the American Hospital Association) have supported developments in medical informatics, with some having greater research expenditures and earlier histories than AHRQ. Nevertheless, it is the Agency''s contributions to medical informatics that are the focus of this study. The purpose of this article is to provide a historical perspective for understanding the benefits of past research funded by AHRQ that supports health care applications of information technology today and that foreshadows AHRQ''s medical informatics initiatives for the future.     

Development of a predictive model for retention in HIV care using natural language processing of clinical notes

ObjectiveAdherence to a treatment plan from HIV-positive patients is necessary to decrease their mortality and improve their quality of life, however some patients display poor appointment adherence and become lost to follow-up (LTFU). We applied natural language processing (NLP) to analyze indications towards or against LTFU in HIV-positive patients’ notes.Materials and MethodsUnstructured lemmatized notes were labeled with an LTFU or Retained status using a 183-day threshold. An NLP and supervised machine learning system with a linear model and elastic net regularization was trained to predict this status. Prevalence of characteristics domains in the learned model weights were evaluated.ResultsWe analyzed 838 LTFU vs 2964 Retained notes and obtained a weighted F1 mean of 0.912 via nested cross-validation; another experiment with notes from the same patients in both classes showed substantially lower metrics. “Comorbidities” were associated with LTFU through, for instance, “HCV” (hepatitis C virus) and likewise “Good adherence” with Retained, represented with “Well on ART” (antiretroviral therapy).DiscussionMentions of mental health disorders and substance use were associated with disparate retention outcomes, however history vs active use was not investigated. There remains further need to model transitions between LTFU and being retained in care over time.ConclusionWe provided an important step for the future development of a model that could eventually help to identify patients who are at risk for falling out of care and to analyze which characteristics could be factors for this. Further research is needed to enhance this method with structured electronic medical record fields.     

Toward a National Framework for the Secondary Use of Health Data: An American Medical Informatics Association White Paper

Secondary use of health data applies personal health information (PHI) for uses outside of direct health care delivery. It includes such activities as analysis, research, quality and safety measurement, public health, payment, provider certification or accreditation, marketing, and other business applications, including strictly commercial activities. Secondary use of health data can enhance health care experiences for individuals, expand knowledge about disease and appropriate treatments, strengthen understanding about effectiveness and efficiency of health care systems, support public health and security goals, and aid businesses in meeting customers’ needs. Yet, complex ethical, political, technical, and social issues surround the secondary use of health data. While not new, these issues play increasingly critical and complex roles given current public and private sector activities not only expanding health data volume, but also improving access to data. Lack of coherent policies and standard “good practices” for secondary use of health data impedes efforts to strengthen the U.S. health care system. The nation requires a framework for the secondary use of health data with a robust infrastructure of policies, standards, and best practices. Such a framework can guide and facilitate widespread collection, storage, aggregation, linkage, and transmission of health data. The framework will provide appropriate protections for legitimate secondary use.     

An approach to predicting patient experience through machine learning and social network analysis

ObjectiveImproving the patient experience has become an essential component of any healthcare system’s performance metrics portfolio. In this study, we developed a machine learning model to predict a patient’s response to the Hospital Consumer Assessment of Healthcare Providers and Systems survey’s “Doctor Communications” domain questions while simultaneously identifying most impactful providers in a network.Materials and MethodsThis is an observational study of patients admitted to a single tertiary care hospital between 2016 and 2020. Using machine learning algorithms, electronic health record data were used to predict patient responses to Hospital Consumer Assessment of Healthcare Providers and Systems survey questions in the doctor domain, and patients who are at risk for responding negatively were identified. Model performance was assessed by area under receiver-operating characteristic curve. Social network analysis metrics were also used to identify providers most impactful to patient experience.ResultsUsing a random forest algorithm, patients’ responses to the following 3 questions were predicted: “During this hospital stay how often did doctors. 1) treat you with courtesy and respect? 2) explain things in a way that you could understand? 3) listen carefully to you?” with areas under the receiver-operating characteristic curve of 0.876, 0.819, and 0.819, respectively. Social network analysis found that doctors with higher centrality appear to have an outsized influence on patient experience, as measured by rank in the random forest model in the doctor domain.ConclusionsA machine learning algorithm identified patients at risk of a negative experience. Furthermore, a doctor social network framework provides metrics for identifying those providers that are most influential on the patient experience.     

Electronic consultations and clinician burnout: An antidote to our emotional pandemic?

Health information technology is a major source of clinician burnout due to increased administrative burden and inefficient work processes. Electronic consultations (eConsults) represent a promising innovation to improve access to specialty care by reducing wait times for specialist visits and reducing unnecessary in-person specialist visits. While eConsults have clear benefits for patients and healthcare systems, their potential effects on provider burnout should be considered. Using a framework which outlines that the loss of autonomy, competence, and relatedness as the main contributing factors to clinician “amotivation” and burnout, we discuss the use of eConsults and their potential to mitigate or exacerbate burnout for primary care providers and specialists, as well as recommendations for implementation of eConsults to reduce burnout.     

NLRP3 inflammasome: a new therapeutic target for high-risk reproductive disorders?

The NOD-like receptor protein 3 (NLRP3) inflammasome is a key regulator of the host''s immune response, and many immune and metabolic disorders are linked to its activation. This review aimed to investigate and clarify the relationship between this inflammasome and high-risk reproductive disorders. Papers cited here were retrieved from PubMed up to August 2020 using the keywords “NLRP3” or “NALP3”, “caspase-1”, “endometriosis”, “gestational diabetes”, “interleukin (IL)-18”, “IL-1β”, “pre-eclampsia (PE)”, “preterm birth”, “polycystic ovarian syndrome (PCOS)”, “recurrent spontaneous abortion (RSA)”, and combinations of these terms. The results show that NLRP3 inflammasome is associated with various high-risk reproductive disorders and many inflammatory factors are secreted during its activation, such as IL-1β induced during the development of endometriosis. PCOS is also associated with activation of the NLRP3 inflammasome, especially in overweight patients. It also participates in the pathogenesis of RSA and is activated in fetal membranes before preterm birth. The placentas of pregnant women with PE show higher expression of the NLRP3 inflammasome, and gestational diabetes mellitus occurs simultaneously with its activation. Current evidence suggest that the NLRP3 inflammasome plays an important role in female reproductive disorders. New treatment and management methods targeting it might help reduce the incidence of such disorders and improve neonatal outcomes.     

Medication information extraction with linguistic pattern matching and semantic rules

Objective

This study presents a system developed for the 2009 i2b2 Challenge in Natural Language Processing for Clinical Data, whose aim was to automatically extract certain information about medications used by a patient from his/her medical report. The aim was to extract the following information for each medication: name, dosage, mode/route, frequency, duration and reason.

Design

The system implements a rule-based methodology, which exploits typical morphological, lexical, syntactic and semantic features of the targeted information. These features were acquired from the training dataset and public resources such as the UMLS and relevant web pages. Information extracted by pattern matching was combined together using context-sensitive heuristic rules.

Measurements

The system was applied to a set of 547 previously unseen discharge summaries, and the extracted information was evaluated against a manually prepared gold standard consisting of 251 documents. The overall ranking of the participating teams was obtained using the micro-averaged F-measure as the primary evaluation metric.

Results

The implemented method achieved the micro-averaged F-measure of 81% (with 86% precision and 77% recall), which ranked this system third in the challenge. The significance tests revealed the system''s performance to be not significantly different from that of the second ranked system. Relative to other systems, this system achieved the best F-measure for the extraction of duration (53%) and reason (46%).

Conclusion

Based on the F-measure, the performance achieved (81%) was in line with the initial agreement between human annotators (82%), indicating that such a system may greatly facilitate the process of extracting relevant information from medical records by providing a solid basis for a manual review process.The 2009 i2b2 medication extraction challenge¹ focused on the extraction of medication-related information including: medication name (m), dosage (do), mode (mo), frequency (f), duration (du) and reason (r) from discharge summaries. In other words, free-text medical records needed to be converted into a structured form by filling a template (a data structure with the predefined slots)² with the relevant information extracted (slot fillers). For example, the following sentence:“In the past two months, she had been taking Ativan of 3–4 mg q.d. for anxiety.”should be converted automatically into a structured form as follows:m=“ativan” ‖ do=“3–4 mg” ‖ mo=“nm” ‖ f=“q.d.” ‖ du=“two months” ‖ r=“for anxiety”Note that only explicitly mentioned information was to be extracted with no attempt to map it to standardized terminology or to interpret it semantically.