Implementing Telemedicine in Primary Care: Learning Lessons From Electronic Health Records.
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ABSTRACT:
SUBMITTER: Sinsky CA
PROVIDER: S-EPMC7449897 | biostudies-literature | 2020 Sep
REPOSITORIES: biostudies-literature
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Implementing Telemedicine in Primary Care: Learning Lessons From Electronic Health Records.
Mayo Clinic proceedings 20200901 9
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Project description:BackgroundDigital health has been a tool of transformation for the delivery of health care services globally. An electronic health record (EHR) system can solve the bottleneck of paper documentation in health service delivery if it is successfully implemented, but poor implementation can lead to a waste of resources. The study of EHR system implementation in low- and middle-income countries (LMICs) is of particular interest to health stakeholders such as policy makers, funders, and care providers because of the efficiencies and evidence base that could result from the appropriate evaluation of such systems.ObjectiveWe aimed to develop a theory of change (ToC) for the implementation of EHRs for maternal and child health care delivery in LMICs. The ToC is an outcomes-based approach that starts with the long-term goals and works backward to the inputs and mediating components required to achieve these goals for complex programs.MethodsWe used the ToC approach for the whole implementation's life cycle to guide the pilot study and identify the preconditions needed to realize the study's long-term goal at Festac Primary Health Centre in Lagos, Nigeria. To evaluate the maturity of the implementation, we adapted previously defined success factors to supplement the ToC approach.ResultsThe initial ToC map showed that the long-term goal was an improved service delivery in primary care with the introduction of EHRs. The revised ToC revealed that the long-term change was the improved maternal and child health care delivery at Festac Primary Health Center using EHRs. We proposed a generic ToC map that implementers in LMICs can use to introduce an optimized EHR system, with assumptions about sustainability and other relevant factors. The outcomes from the critical success factors were sustainability: the sustained improvements included trained health care professionals, a change in mindset from using paper systems toward digital health transformation, and using the project's laptops to collect aggregate data for the District Health Information System 2-based national health information management system; financial: we secured funding to procure IT equipment, including servers, laptops, and networking, but the initial cost of implementation was high, and funds mainly came from the funding partner; and organizational: the health professionals, especially the head of nursing and health information officers, showed significant commitment to adopting the EHR system, but certain physicians and midwives were unwilling to use the EHR system initially until they were persuaded or incentivized by the management.ConclusionsThis study shows that the ToC is a rewarding approach to framing dialogue with stakeholders and serves as a framework for planning, evaluation, learning, and reflection. We hypothesized that any future health IT implementation in primary care could adapt our ToC approach to their contexts with necessary modifications based on inherent characteristics.
| S-EPMC9412900 | biostudies-literature
Project description:ObjectiveTo present a partnership-based and community-oriented approach designed to ease provider anxiety and facilitate the implementation of electronic health records (EHR) in resource-limited primary care settings.Materials and methodsThe approach, referred to as partnership model, was developed and iteratively refined through the research team's previous work on implementing health information technology (HIT) in over 30 safety net practices. This paper uses two case studies to illustrate how the model was applied to help two nurse-managed health centers (NMHC), a particularly vulnerable primary care setting, implement EHR and get prepared to meet the meaningful use criteria.ResultsThe strong focus of the model on continuous quality improvement led to eventual implementation success at both sites, despite difficulties encountered during the initial stages of the project.DiscussionThere has been a lack of research, particularly in resource-limited primary care settings, on strategies for abating provider anxiety and preparing them to manage complex changes associated with EHR uptake. The partnership model described in this paper may provide useful insights into the work shepherded by HIT regional extension centers dedicated to supporting resource-limited communities disproportionally affected by EHR adoption barriers.ConclusionNMHC, similar to other primary care settings, are often poorly resourced, understaffed, and lack the necessary expertise to deploy EHR and integrate its use into their day-to-day practice. This study demonstrates that implementation of EHR, a prerequisite to meaningful use, can be successfully achieved in this setting, and partnership efforts extending far beyond the initial software deployment stage may be the key.
| S-EPMC3197990 | biostudies-literature
Project description:Background The efficiency of the patient care process of short-term medical service trips is often not assessed. The Gregory School of Pharmacy has organized annual medical camps in rural Uganda, however, the paper health records used for documentation and communication between stations have shown several limitations that hinder an optimal patient care process. Therefore, our objective was to implement an electronic health record system in these medical camps to improve the workflow and optimize the patient care process. Methods An electronic health record system that functioned over a battery-operated local area network was developed and implemented. Patient health information was entered and reviewed at the different stations using mobile devices. The impact of electronic health records (used in 2019) on the patient care process was assessed using the number of patients served per physician per hour and the number of prescriptions filled per hour and comparing these to paper records (used in 2017). Results Electronic health records were successfully implemented and communication across stations was fluid, thus improving transitions. Importantly, 45% more patients were served per physician per hour and 38% more prescriptions were dispensed per hour when using electronic (2019) compared to paper records (2017), despite having a smaller team in 2019. Conclusion Implementation of electronic health records in rural Uganda improved the patient care process and the efficiency of the medical camp.
| S-EPMC10012798 | biostudies-literature
Project description:BackgroundSeveral barriers to the adoption of electronic medical records (EMRs) by family physicians have been discussed, including the costs of implementation, impact on work flow and loss of productivity. We examined billings and payments received before and after implementation of EMRs among primary care physicians in the province of Ontario. We also examined billings and payments before and after switching from a fee-for-service to a capitation payment model, because EMR implementation coincided with primary care reform in the province.MethodsWe used information from the Electronic Medical Record Administrative Data Linked Database (EMRALD) to conduct a retrospective before-after study. The EMRALD database includes EMR data extracted from 183 community-based family physicians in Ontario. We included EMRALD physicians who were eligible to bill the Ontario Health Insurance Plan at least 18 months before and after the date they started using EMRs and had completed a full 18-month period before Mar. 31, 2011, when the study stopped. The main outcome measures were physicians' monthly billings and payments for office visits and total annual payments received from all government sources. Two index dates were examined: the date physicians started using EMRs and were in a stable payment model (n = 64) and the date physicians switched from a fee-for-service to a capitation payment model (n = 42).ResultsMonthly billings and payments for office visits did not decrease after the implementation of EMRs. The overall weighted mean annual payment from all government sources increased by 27.7% after the start of EMRs among EMRALD physicians; an increase was also observed among all other primary care physicians in Ontario, but it was not as great (14.4%). There was a decline in monthly billings and payments for office visits after physicians changed payment models, but an increase in their overall annual government payments.InterpretationImplementation of EMRs by primary care physicians did not result in decreased billings or government payments for office visits. Further economic analyses are needed to measure the effects of EMR implementation on productivity and the costs of implementing an EMR system, including the costs of nonclinical work by physicians and their staff.
| S-EPMC3985899 | biostudies-literature
Project description:ObjectiveThe quality of alcohol-related prevention and treatment in US primary care is poor. The purpose of this study was to describe the extent to which Electronic Health Records (EHRs) used by 167 primary care practices across 7 states currently include the necessary prompts, clinical support, and performance reporting essential for improving alcohol-related prevention and treatment in primary care.Materials and methodsExperts from five regional quality improvement programs identified basic EHR features needed to support evidence-based alcohol-related prevention (ie, screening and brief intervention) and treatment of alcohol use disorders (AUD). Data were collected regarding whether EHRs included these features.ResultsEHRs from 21 vendors were used by the primary care practices. For prevention, 62% of the 167 practices' EHRs included a validated screening questionnaire, 46% automatically scored the screening instrument, 62% could report the percent screened, and 37% could report the percent screening positive. Only 7% could report the percent offered brief intervention. For alcohol treatment, 49% of practices could report the percent diagnosed with AUD, 58% and 91% allowed documentation of referral and treatment with AUD medication, respectively. Only 3% could report the percent of patients diagnosed with AUD who received treatment.DiscussionMost EHRs observed across 167 primary care practices across 7 US states lacked basic functionality necessary to support evidence-based alcohol-related prevention and AUD treatment. Only 3% and 7% of EHRs, respectively, included the ability to report widely recommended quality measures needed to improve the quality of recommended alcohol-related prevention and treatment in primary care.ConclusionImproving EHR functionality is likely necessary before alcohol-related primary care can be improved.
| S-EPMC11630038 | biostudies-literature
Project description:Importancerisk factors for delirium in hospital inpatients are well established, but less is known about whether delirium occurring in the community or during an emergency admission to hospital care might be predicted from routine primary-care records.Objectivesidentify risk factors in primary-care electronic health records (PC-EHR) predictive of delirium occurring in the community or recorded in the initial episode in emergency hospitalisation. Test predictive performance against the cumulative frailty index.DesignStage 1: case-control; Stages 2 and 3: retrospective cohort.Settingclinical practice research datalink: PC-EHR linked to hospital discharge data from England.SubjectsStage 1: 17,286 patients with delirium aged ≥60 years plus 85,607 controls. Stages 2 and 3: patients ≥ 60 years (n = 429,548 in 2015), split into calibration and validation groups.MethodsStage 1: logistic regression to identify associations of 110 candidate risk measures with delirium. Stage 2: calibrating risk factor weights. Stage 3: validation in independent sample using area under the curve (AUC) receiver operating characteristic.Resultsfifty-five risk factors were predictive, in domains including: cognitive impairment or mental illness, psychoactive drugs, frailty, infection, hyponatraemia and anticholinergic drugs. The derived model predicted 1-year incident delirium (AUC = 0.867, 0.852:0.881) and mortality (AUC = 0.846, 0.842:0.853), outperforming the frailty index (AUC = 0.761, 0.740:0.782). Individuals with the highest 10% of predicted delirium risk accounted for 55% of incident delirium over 1 year.Conclusionsa risk factor model for delirium using data in PC-EHR performed well, identifying individuals at risk of new onsets of delirium. This model has potential for supporting preventive interventions.
| S-EPMC7297278 | biostudies-literature
Project description:ObjectivesBetter indicators from affordable, sustainable data sources are needed to monitor population burden of musculoskeletal conditions. We propose five indicators of musculoskeletal health and assessed if routinely available primary care electronic health records (EHR) can estimate population levels in musculoskeletal consulters.MethodsWe collected validated patient-reported measures of pain experience, function and health status through a local survey of adults (≥35 years) presenting to English general practices over 12 months for low back pain, shoulder pain, osteoarthritis and other regional musculoskeletal disorders. Using EHR data we derived and validated models for estimating population levels of five self-reported indicators: prevalence of high impact chronic pain, overall musculoskeletal health (based on Musculoskeletal Health Questionnaire), quality of life (based on EuroQoL health utility measure), and prevalence of moderate-to-severe low back pain and moderate-to-severe shoulder pain. We applied models to a national EHR database (Clinical Practice Research Datalink) to obtain national estimates of each indicator for three successive years.ResultsThe optimal models included recorded demographics, deprivation, consultation frequency, analgesic and antidepressant prescriptions, and multimorbidity. Applying models to national EHR, we estimated that 31.9% of adults (≥35 years) presenting with non-inflammatory musculoskeletal disorders in England in 2016/17 experienced high impact chronic pain. Estimated population health levels were worse in women, older aged and those in the most deprived neighbourhoods, and changed little over 3 years.ConclusionNational and subnational estimates for a range of subjective indicators of non-inflammatory musculoskeletal health conditions can be obtained using information from routine electronic health records.
| S-EPMC8487274 | biostudies-literature
Project description:BackgroundWe aimed to identify and characterise the longitudinal patterns of multimorbidity associated with stroke.MethodsWe used an unsupervised patient-oriented clustering approach to analyse primary care electronic health records (EHR) of 30 common long-term conditions (LTC) in patients with stroke aged over 18, registered in 41 general practices in south London between 2005 and 2021.ResultsOf 849,968 registered patients, 9,847 (1.16%) had a record of stroke and 46.5% were female. The median age at record of stroke was 65.0 year (IQR: 51.5-77.0) and the median number of LTCs in addition to stroke was 3 (IQR: 2-5). We identified eight clusters of multimorbidity with contrasted socio-demographic characteristics (age, gender, and ethnicity) and risk factors. Beside a core of 3 clusters associated with conventional stroke risk-factors, minor clusters exhibited less common combinations of LTCs including mental health conditions, asthma, osteoarthritis and sickle cell anaemia. Importantly, complex profiles combining mental health conditions, infectious diseases and substance dependency emerged.ConclusionThis novel longitudinal and patient-oriented perspective on multimorbidity addresses existing gaps in mapping the patterns of stroke-associated multimorbidity not only in terms of LTCs, but also socio-demographic characteristics, and suggests potential for more efficient and patient-oriented healthcare models.
| S-EPMC11656854 | biostudies-literature
Project description:BackgroundParoxysmal Nocturnal Haemoglobinuria (PNH) is an ultra-rare, acquired disorder that is challenging to diagnose due to varied symptoms, heterogeneous patient presentations, and lack of awareness among healthcare professionals. This leads to frequent misdiagnosis and delays in diagnosis. This study evaluated the feasibility of a machine learning model to identify undiagnosed PNH patients using structured electronic health records.MethodsThe study used data from the Optimum Patient Care Research Database, which contains electronic health records from general practitioner (GP) practices across the United Kingdom. PNH patients were identified by the presence, and control patients by the absence of a PNH diagnosis code in their records. Clinical features (symptoms, diagnoses, healthcare utilisation) from 131 patients in the PNH group and 593,838 patients in the control group, were inputted to a tree-based XGBoost machine learning model to classify patients as either "positive" or "negative" for PNH suspicion. The algorithm was finalised after additional exclusions and inclusions applied. Performance was assessed using positive predictive value (PPV), recall and specificity. As the sample used to develop the algorithm was not representative of the true population prevalence, PPV was additionally adjusted to reflect performance in the wider population.ResultsOf all the patients in the PNH group, 27% were classified as positive (recall). 99.99% of the control group were classified as negative (specificity). Of all the patients classified as positive, 60.4% had a diagnosis of PNH in their record (PPV). The PPV adjusted for the population prevalence of PNH was 19.59 suggesting nearly 1 in 5 patients flagged may warrant further PNH investigation. The key clinical features in the model were aplastic anaemia, pancytopenia, haemolytic anaemia, myelodysplastic syndrome, and Budd-Chiari syndrome.ConclusionThis is the first study to combine clinical understanding of PNH with machine learning, demonstrating the ability to discriminate between PNH and control patients in retrospective electronic health records. With further investigation and validation, this algorithm could be deployed on live health data, potentially leading to earlier diagnosis for patients who currently experience long diagnostic delays or remain undiagnosed.
| S-EPMC11479535 | biostudies-literature
Project description:BackgroundThe mission of medical schools is a sustainable commitment to orient education, research, and services based on the priorities and expectations of society. The most common complaints of patients from comprehensive health service centers (CHSCs) based on the data from electronic health records were assessed in order to determine primary health care (PHC) priorities for the educational planning of medical students in Iran.MethodsA population-based national study was designed to assess clinical complaints of patients in all age groups who were referred to CHSCs at least once to be visited by physicians. All the data in the census were extracted from electronic health records in PHC system during 2015-2020, classified by the International Classification of Primary Care 2nd edition (ICPC-2e-English), and statistically analyzed. The total number of complaints that were recorded in the system was 17,430,139.Results59% of the referring patients were women. The highest number of referrals was related to the age group of 18-59 years (56.9%), while the lowest belonged to the elderly people (13.3%). In all age and sex groups, the first ten complaints of patients with three top priorities in each category included process (follow-up, consultation, and results exam), digestive (toothache and gum complaint, abdominal pain, and diarrhea), respiratory (cough, sore throat, and runny nose), general (fever, pain, and weakness and fatigue), musculoskeletal (back pain, leg complaint, and knee injuries), endocrine and nutritional (weight gain, Feeding problem, and weight loss), cardiovascular (hypertension, palpitations, and Postural hypotension), neurological (headache, dizziness, and paralysis), sexual dysfunction (vaginal complaint, discharge, and irregular menstruation), and dermatological (pruritus, rash, and inflammation) problems.ConclusionHigh priorities in referring to PHC had a key role in assessing the country's health needs. Since this study was in line with the national pattern of complaints and patients' profile, the present findings can be helpful to amend policy-making, educational planning and curricula development in medical schools.
| S-EPMC9008379 | biostudies-literature