Delinquent Medical Records: Who Are the Stakeholders for Timely Medical Documentation?

The explosion of electronic documentation associated with Meaningful Use-certified electronic health record systems has led to a massive increase in provider workload for completion and finalization of patient encounters. Delinquency of required documentation affects multiple areas of hospital operations. We present the major stakeholders affected by delinquency of the electronic medical record and examine the differing perspectives to gain insight for successful engagement to reduce the burden of medical record delinquency.

Association of Electronic Health Record Use Above Meaningful Use Thresholds With Hospital Quality and Safety Outcomes.

Importance: By 2018, Medicare spent more than $30 billion to incentivize the adoption of electronic health records (EHRs), based partially on the belief that EHRs would improve health care quality and safety. In a time when most hospitals are well past minimum meaningful use (MU) requirements, examining whether EHR implementation beyond the minimum threshold is associated with increased quality and safety may guide the future focus of EHR development and incentive structures. Objective: To determine whether EHR implementation above MU performance thresholds is associated with changes in hospital patient satisfaction, efficiency, and safety. Design, Setting, and Participants: This quantile regression analysis of cross-sectional data used publicly available data sets from 2362 acute care hospitals in the United States participating in both the MU and Hospital Value-Based Purchasing (HVBP) programs from January 1 to December 31, 2016. Data were analyzed from August 1, 2019, to May 22, 2020. Exposures: Seven MU program performance measures, including medication and laboratory orders placed through the EHR, online health information availability and access rates, medication reconciliation through the EHR, patient-specific educational resources, and electronic health information exchange. Main Outcomes and Measures: The HVBP outcomes included patient satisfaction survey dimensions, Medicare spending per beneficiary, and 5 types of hospital-acquired infections. Results: Among the 2362 participating hospitals, mixed associations were found between MU measures and HVBP outcomes, all varying by outcome quantile and in some cases by interaction with EHR vendor. Computerized provider order entry (CPOE) for laboratory orders was associated with decreased ratings of every patient satisfaction outcome at middle quantiles (communication with nurses: ß = -0.33 [P = .04]; communication with physicians: ß = -0.50 [P < .001]; responsiveness of hospital staff: ß = -0.57 [P = .03]; care transition performance: ß = -0.66 [P < .001]; communication about medicines: ß = -0.52 [P = .002]; cleanliness and quietness: ß = -0.58 [P = .007]; discharge information: ß = -0.48 [P < .001]; and overall rating: ß = -0.95 [P < .001]). However, at middle quantiles, CPOE for medication orders was associated with increased ratings for communication with physicians (τ = 0.5; ß = 0.54; P = .009), care transition (τ = 0.5; ß = 1.24; P < .001), discharge information (τ = 0.5; ß = 0.41; P = .01), and overall hospital ratings (τ = 0.5; ß = 0.97; P = .02). At high quantiles, electronic health information exchange was associated with improved ratings of communication with nurses (τ = 0.9; ß = 0.23; P = .03). Medication reconciliation had positive associations with increased communication with nursing at low quantiles (τ = 0.1; ß = 0.60; P < .001), increased discharge information at middle quantiles (τ = 0.5; ß = 0.28; P = .03), and responsiveness of hospital staff at middle (τ = 0.5; ß = 0.77; P = .001) and high (τ = 0.9; ß = 0.84; P = .001) quantiles. Patients accessing their health information online was not associated with any outcomes. Increased use of patient-specific educational resources identified through the EHR was associated with increased ratings of communication with physicians at high quantiles (τ = 0.9; ß = 0.20; P = .02) and with decreased spending at low-spending hospitals (τ = 0.1; ß = -0.40; P = .008). Conclusions and Relevance: Increasing EHR implementation, as measured by MU criteria, was not straightforwardly associated with increased HVBP measures of patient satisfaction, spending, and safety in this study. These results call for a critical evaluation of the criteria by which EHR implementation is measured and increased attention to how different EHR products may lead to differential outcomes.

A mid-South chronic disease registry and practice-based research network to address disparities.

OBJECTIVES: To describe an innovative health information technology (HIT) model for supporting community-wide health improvement through multiprovider collaboration in a regional population health registry and practice-based research network (PBRN). STUDY DESIGN: Case study. METHODS: We describe the HIT data structure and governance of the Diabetes Wellness and Prevention Coalition (DWPC) Registry and PBRN based in Memphis, Tennessee. The population served and their characteristics were assessed for all adult patients with at least 1 encounter in a participating health care delivery system from January 1, 2013, to March 31, 2019. Disparities in access and health care utilization were assessed by residential zip code. RESULTS: The DWPC Registry is a chronic disease and population health data warehouse designed to facilitate chronic disease surveillance and tracking of processes and outcomes of care in medically underserved areas of the mid-South. The Registry primarily focuses on obesity-associated chronic conditions such as diabetes, hypertension, hyperlipidemia, and chronic kidney disease. It combines patient data from 7 regional health systems, which include 6 adult hospitals and more than 50 outpatient practices, covering 462,223 adults with 2,032,425 clinic visits and 602,679 hospitalizations and/or emergency department visits from January 1, 2013, to March 31, 2019. The most prevalent chronic conditions include obesity (37.2%), hypertension (34.4%), overweight (26.4%), hyperlipidemia (18.0%), and type 2 diabetes (14.0%). The Registry provides quarterly practice improvement reports to participating clinics, facilitates surveillance of and outreach to patients with unmet health needs, and supports a pragmatic clinical trial and multiple cohort studies. CONCLUSIONS: Regional registries and PBRNs are powerful tools that can support real-world quality improvement and population health efforts to reduce disparities and improve equity in chronic disease care in medically underserved communities across the United States.

Moving from Quality Measurement to Quality Improvement: Applying Meaningful Use Lessons to the Quality Payment Program.

Although the federal electronic health record (EHR) incentive program has ended, the need to effectively implement and use EHRs has not. The advent of the federal Quality Payment Program (QPP) has made effective use of EHRs more critical than ever, especially for clinical quality measurement and improvement. However, practices continue to face challenges in successfully implementing and using EHRs to achieve these aims. We used a multiple case study approach to understand how physician practices were using EHR data to measure and improve quality. We interviewed a variety of physicians and staff at multiple practices of diverse sizes and settings. Our findings suggest specific approaches that can help practices better harness their EHR data to measure and improve the quality of care while reducing or preventing staff dissatisfaction and burnout. These lessons can help practices better leverage their EHRs to succeed in the QPP.

Information System Maturity Models in Healthcare.

The use of information systems in healthcare (HIS) has been recognised as having crucial importance in improving the efficiency, cost-effectiveness, quality, and safety of medical care delivery. HIS has the potential to improve individuals' health and providers' performance by producing better quality, cost savings, and greater patient involvement in their own health. There have been two major drivers for the HIS investments in healthcare: The ever-increasing burden from chronic disease with costs growing significantly faster and the recognition of the need for greatly improved quality and safety in health delivery. Maturity models (MM) are based on the premises that people, organizations, functional areas and processes evolve through a process of development or growth towards a more advanced maturity, going through a distinct number of levels. Through a state-of-the-art review of HIS, focused on their maturity state, we identify and characterize a set of critical factors recognized as determinants in the context of HIS maturity. The article identifies a broad spectrum of MM applied to the health sector and its characteristics and reinforces the belief that the maturity of HIS can contribute to the quality of information and knowledge management in the sector.

Concurrence of big data analytics and healthcare: A systematic review.

BACKGROUND: The application of Big Data analytics in healthcare has immense potential for improving the quality of care, reducing waste and error, and reducing the cost of care. PURPOSE: This systematic review of literature aims to determine the scope of Big Data analytics in healthcare including its applications and challenges in its adoption in healthcare. It also intends to identify the strategies to overcome the challenges. DATA SOURCES: A systematic search of the articles was carried out on five major scientific databases: ScienceDirect, PubMed, Emerald, IEEE Xplore and Taylor & Francis. The articles on Big Data analytics in healthcare published in English language literature from January 2013 to January 2018 were considered. STUDY SELECTION: Descriptive articles and usability studies of Big Data analytics in healthcare and medicine were selected. DATA EXTRACTION: Two reviewers independently extracted information on definitions of Big Data analytics; sources and applications of Big Data analytics in healthcare; challenges and strategies to overcome the challenges in healthcare. RESULTS: A total of 58 articles were selected as per the inclusion criteria and analyzed. The analyses of these articles found that: (1) researchers lack consensus about the operational definition of Big Data in healthcare; (2) Big Data in healthcare comes from the internal sources within the hospitals or clinics as well external sources including government, laboratories, pharma companies, data aggregators, medical journals etc.; (3) natural language processing (NLP) is most widely used Big Data analytical technique for healthcare and most of the processing tools used for analytics are based on Hadoop; (4) Big Data analytics finds its application for clinical decision support; optimization of clinical operations and reduction of cost of care (5) major challenge in adoption of Big Data analytics is non-availability of evidence of its practical benefits in healthcare. CONCLUSION: This review study unveils that there is a paucity of information on evidence of real-world use of Big Data analytics in healthcare. This is because, the usability studies have considered only qualitative approach which describes potential benefits but does not take into account the quantitative study. Also, majority of the studies were from developed countries which brings out the need for promotion of research on Healthcare Big Data analytics in developing countries.

Aging barriers influencing mobile health usability for older adults: A literature based framework (MOLD-US).

BACKGROUND: With the growing population of older adults as a potential user group of mHealth, the need increases for mHealth interventions to address specific aging characteristics of older adults. The existence of aging barriers to computer use is widely acknowledged. Yet, usability studies show that mHealth still fails to be appropriately designed for older adults and their expectations. To enhance designs of mHealth aimed at older adult populations, it is essential to gain insight into aging barriers that impact the usability of mHealth as experienced by these adults. OBJECTIVES: This study aims to synthesize literature on aging barriers to digital (health) computer use, and explain, map and visualize these barriers in relation to the usability of mHealth by means of a framework. METHODS: We performed a scoping review to synthesize and summarize reported physical and functional age barriers in relation to digital (mobile) health applications use. Aging barriers reported in the literature were mapped onto usability aspects categorized by Nielsen to explain their influence on user experience of mHealth. A framework (MOLD-US) was developed summarizing the evidence on the influence of aging barriers on mHealth use experienced by older adults. RESULTS: Four key categories of aging barriers influencing usability of mHealth were identified: cognition, motivation, physical ability and perception. Effective and satisfactory use of mHealth by older adults is complicated by cognition and motivation barriers. Physical ability and perceptual barriers further increase the risk of user errors and fail to notice important interaction tasks. Complexities of medical conditions, such as diminished eye sight related to diabetes or deteriorated motor skills as a result of rheumatism, can cause errors in user interaction. CONCLUSIONS: This research provides a novel framework for the exploration of aging barriers and their causes influencing mHealth usability in older adults. This framework allows for further systematic empirical testing and analysis of mHealth usability issues, as it enables results to be classified and interpreted based on impediments intrinsic to usability issues experienced by older adults. Importantly, the paper identifies a key need for future research on motivational barriers impeding mhealth use of older adults. More insights are needed in particular to disaggregating normal age related functional changes from specific medical conditions that influence experienced usefulness of mHealth by these adults.

[Meaning and humanity in care]. / Sens et humanité dans le soin.

Nursing practice is founded on numerous theories. While life sciences use evidence to design the most effective practices from a biomedical point of view, human sciences sometimes struggle to corroborate the perception of patients as caregivers in their humanised practice. This article presents a perspective of care in a resolutely human dimension, where the main objective is to be of use to the person.

Assessing the value of the Meaningful Use Clinical Summary for patients and families with pediatric asthma.

Electronic clinical summaries are innovations supported by the Electronic Health Record Incentive Program, known as "Meaningful Use" (MU). The MU clinical summary documents the shared understanding of the plan of care for patients and assists families in managing asthma-related health care. The purpose of this analysis was to identify the communicative value of the summaries to patients and families. Readability measurements, content analysis, and descriptive statistics were employed in a review of twenty clinical summaries and compared with provider encounter notes. The average age of the patients from whom we collected clinical summaries was six years old. The average reading level of the summaries was ninth grade. Neither summaries nor health education contained visual images. There was a total of nine different asthma diagnoses. A full list of diagnoses was present in 45% of summaries. The average medications per patient was 5.75, and there were multiple medication changes noted (dosage adjustment, add, discontinued). Allergies, vital signs, and smoking status were reliably reported (99-100%). Provider orders present included medication, follow up, and return to the clinic instructions. The plan of care was replicated on 45% of summaries. There was variable reporting of various asthma guidelines. Opportunities to improve the clinical summary include using plain language to promote readability, action, understanding, and health literacy, training providers to standardize their documentation and include asthma action plans, and configuring EHR settings to ensure diagnoses and plan of care is carried over from provider notes to the summary.

Using Bioinformatics to Treat Hospitalized Smokers: Successes and Challenges of a Tobacco Treatment Service.

BACKGROUND: Hospitals face increasing regulations to provide and document inpatient tobacco treatment, yet few blueprint data exist to implement a tobacco treatment service (TTS). METHODS: A hospitalwide, opt-out TTS with three full-time certified counselors was developed in a large tertiary care hospital to proactively treat smokers according to Chronic Care Model principles and national treatment guidelines. A bioinformatics platform facilitated integration into the electronic health record to meet evolving Centers for Medicare & Medicaid Services meaningful use and Joint Commission standards. TTS counselors visited smokers at the bedside and offered counseling, recommended smoking cessation medication to be ordered by the primary clinical service, and arranged for postdischarge resources. RESULTS: During a 3.5-year span, 21,229 smokers (31,778 admissions) were identified; TTS specialists reached 37.4% (7,943), and 33.3% (5,888) of daily smokers received a smoking cessation medication order. Adjusted odds ratios (AORs) of receiving a chart order for smoking cessation medication during the hospital stay and at discharge were higher among patients the TTS counseled > 3 minutes and recommended medication: inpatient AOR = 7.15 (95% confidence interval [CI] = 6.59-7.75); discharge AOR = 5.3 (95% CI = 4.71-5.97). As implementation progressed, TTS counseling reach and medication orders increased. To assess smoking status ≤ 1 month postdischarge, three methods were piloted, all of which were limited by low follow-up rates (4.5%-28.6%). CONCLUSION: The TTS counseled approximately 3,000 patients annually, with increases over time for reach and implementation. Remaining challenges include the development of strategies to engage inpatient care teams to follow TTS recommendations, and patients postdischarge in order to optimize postdischarge smoking cessation.

Integration of Hospital Information and Clinical Decision Support Systems to Enable the Reuse of Electronic Health Record Data.

BACKGROUND: The efficiency and acceptance of clinical decision support systems (CDSS) can increase if they reuse medical data captured during health care delivery. High heterogeneity of the existing legacy data formats has become the main barrier for the reuse of data. Thus, we need to apply data modeling mechanisms that provide standardization, transformation, accumulation and querying medical data to allow its reuse. OBJECTIVES: In this paper, we focus on the interoperability issues of the hospital information systems (HIS) and CDSS data integration. MATERIALS AND METHODS: Our study is based on the approach proposed by Marcos et al. where archetypes are used as a standardized mechanism for the interaction of a CDSS with an electronic health record (EHR). We build an integration tool to enable CDSSs collect data from various institutions without a need for modifications in the implementation. The approach implies development of a conceptual level as a set of archetypes representing concepts required by a CDSS. RESULTS: Treatment case data from Regional Clinical Hospital in Tomsk, Russia was extracted, transformed and loaded to the archetype database of a clinical decision support system. Test records' normalization has been performed by defining transformation and aggregation rules between the EHR data and the archetypes. These mapping rules were used to automatically generate openEHR compliant data. After the transformation, archetype data instances were loaded into the CDSS archetype based data storage. The performance times showed acceptable performance for the extraction stage with a mean of 17.428 s per year (3436 case records). The transformation times were also acceptable with 136.954 s per year (0.039 s per one instance). The accuracy evaluation showed the correctness and applicability of the method for the wide range of HISes. These operations were performed without interrupting the HIS workflow to prevent the HISes from disturbing the service provision to the users. CONCLUSIONS: The project results have proven that archetype based technologies are mature enough to be applied in routine operations that require extraction, transformation, loading and querying medical data from heterogeneous EHR systems. Inference models in clinical research and CDSS can benefit from this by defining queries to a valid data set with known structure and constraints. The standard based nature of the archetype approach allows an easy integration of CDSSs with existing EHR systems.

Meaningful Activity for Long-Term Care Residents With Dementia: A Comparison of Activities and Raters.

Purpose of the Study: Engagement in meaningful activities is associated with positive outcomes for persons with dementia, yet studies demonstrating quantitative evidence for which activities can be considered meaningful are lacking. We investigated MemPics™, a program designed to promote meaningful activity for individuals with dementia through engagement and cognitive stimulation. It was compared with other recreation activities offered in U.S. long-term care facilities to determine whether MemPics™ was rated as having more meaningful activity from both the perspectives of participants and recreation staff. Design and Methods: Long-term care residents with mild to moderate dementia that met eligibility criteria were randomly assigned to an activity group (treatment, control). Participants completed 2 sessions of either the experimental or control group activity with facility recreation staff. Both participants and staff rated each activity in terms of meaningfulness following each session. Results: Of the enrolled participants (N = 126), study analyses were based on the 94 participants (n = 48 treatment and n = 46 control; M age = 82.98±9.63) who completed the activity sessions. Compared to the control group activities, MemPics™ had significantly higher participant and staff ratings of meaningfulness. Scores between the 2 rater types were significantly different, with staff reporting higher meaningful activity than participants. Further support for MemPics™ was found in exit survey responses from participating staff. Implications: We discuss the merits and shortcoming of this study, the utility of MemPics™ for providing meaningful engagement in long-term care residents with mild to moderate dementia, and ideas for future research.

Medicaid Stage 1 Meaningful Use EHR Incentive Payments Are Associated With Higher Quality but Not Improvements in Quality.

This was a retrospective cohort study of ambulatory care quality by physicians who received payment for Medicaid Stage 1 Meaningful Use (MU) in 2012 using New York State Medicaid Claims (2010-2013). Eight quality measures were used to compare performance of physicians who received payments to Adopt, Implement, or Use (AIU) an electronic health record in 2011 but not for MU in 2012 (AIU-only group) and physicians who cared for Medicaid patients but received no payments (no-incentive group), using propensity score-weighted difference-in-difference logistic regression analyses, clustering by physician. In all, 13 697 physicians and 913 476 patients were studied. In 2010, the MU group scored higher than both groups (vs AIU-only in 3 of 8 measures, 0.8-1.3 adjusted percentage points; vs no-incentive, 2 of 8 measures, 0.9-2.0 adjusted percentage points). The difference-in-difference analysis found no additional improvements in quality over time relative to either control group. Longer follow-up is needed to determine the effects of Stage 2 MU.

Meaningful Use in Chronic Care: Improved Diabetes Outcomes Using a Primary Care Extension Center Model.

BACKGROUND: The effect of practice facilitation that provides onsite quality improvement (QI) and electronic health record (EHR) coaching on chronic care outcomes is unclear. This study evaluates the effectiveness of such a program-similar to an agricultural extension center model-that provides these services. METHODS: Through the Health Information Technology for Economic and Clinical Health (HITECH) portion of the American Recovery and Reinvestment Act, the North Carolina Area Health Education Centers program became the Regional Extension Center for Health Information Technology (REC) for North Carolina. The REC program provides onsite technical assistance to help small primary care practices achieve meaningful use of certified EHRs. While pursuing meaningful use functionality, practices were also offered complementary onsite advice regarding QI issues. We followed the first 50 primary care practices that utilized both EHR and QI advice targeting diabetes care. RESULTS: The achievement of meaningful use of certified EHRs and performance of QI with onsite practice facilitation showed an absolute improvement of 19% in the proportion of patients who achieved excellent diabetes control (hemoglobin A1c < 7%) compared to baseline. In addition, the percentages of patients with poorly controlled diabetes (hemoglobin A1c > 9%) fell steeply in these practices. LIMITATIONS: No control group was available for comparison. CONCLUSION: Practice facilitation that provided EHR and QI coaching support showed important improvements in diabetes outcomes in practices that achieved meaningful use of their EHR systems. This approach holds promise as a way to help small primary care practices achieve excellent patient outcomes.

Computerization of a Nursing Chart According to the Nursing Process.

The benefits associated with the computerization of clinical records are known since a long time ago. Documentation evolution from paper to electronic format aims to always improve communication, reduce errors and facilitate continuity of care. Ideally when improvements to nursing records are contemplated, they should consider the nurses needs, new functionality workflow impacts and correspondence with representation models of standardized data that are specific to their domains practices. The aim of this study was to describe the development and implementation of computerized nursing record at Hospital Italiano de Buenos Aires.

Analytic Strategies of Streaming Data for eHealth.

New analytic strategies for streaming big data from wearable devices and social media are emerging in ehealth. We face challenges to find meaningful patterns from big data because researchers face difficulties to process big volume of streaming data using traditional processing applications.1 This introductory 180 minutes tutorial offers hand-on instruction on analytics2 (e.g., topic modeling, social network analysis) of streaming data. This tutorial aims to provide practical strategies of information on reducing dimensionality using examples of big data. This tutorial will highlight strategies of incorporating domain experts and a comprehensive approach to streaming social media data.

MonDossierMedical.ch - The Personal Health Record for Every Geneva Citizen.

MonDossierMedical.ch is a project led by the canton of Geneva, making it possible for every patient to access his own electronic health record (EHR) and to share the medical files with his doctors. It was introduced across the canton in mid-2013, and provided to all patients free of charge. It is based on the first Swiss-wide eHealth-compliant pilot project "e-toile". The canton of Geneva developed "e-toile" as a public-private partnership together with Swiss Post and it was launched in 2011 in some of the canton's municipalities. Back then, Geneva's EHR represented the first Swiss attempt to link all healthcare professionals in the treatment chain. Today, it serves more than 6,000 patients and 400 physicians. This number is growing regularly, as well as the health care institutions (private hospitals, labs) joining the community. The project fits into the national strategy of Switzerland in establishing a national EHR by linking regional implementations like MonDossierMedical.

Information Challenges in Patient Transition.

The aim of this workshop is to address challenges in access to accurate and relevant information for both patients and their clinicians during patient transitions. Successful exchange of information across transitions relies on clinicians' use of the electronic health record, information systems that support workflow and communication and access to actionable information to facilitate information exchange. In this workshop the speakers will illustrate the above mentioned challenges. The chairman will initially make a short statement of the purpose and the organization of the workshops.

Intelligent Re-Use of Nursing Routine Data: Opportunities and Challenges.

Secondary use of structured nursing routine data receives an increasing attention in healthcare and is supposed to bear huge potential for different purposes. However, building and analyzing such integrated nursing routine data repositories are nontrivial, challenging tasks. The workshop gives an insight in the state of the art of secondary data analysis in nursing and addresses possible opportunities as well as the main challenges when re-using nursing data for secondary analyses. The target audience of the workshop comprises all stakeholders who are interested in the intelligent re-use of nursing data (e.g. decision-makers, public health officials, nursing managers, nursing informatics/IT staff, scientists, data analysts as well as industry representatives).