Dataset Information

Impact of the meaningful use incentive program on electronic health record adoption by US children's hospitals.

ABSTRACT:

Objective

We determined adoption rates of pediatric-oriented electronic health record (EHR) features by US children's hospitals and assessed perceptions regarding the suitability of commercial EHRs for pediatric care and the influence of the meaningful use incentive program on implementation of pediatric-oriented features.

Materials and methods

We surveyed members of the Children's Hospital Association. We measured adoption of 19 pediatric-oriented features and asked whether commercial EHRs include key pediatric-focused capabilities. We inquired about the meaningful use program's relevance to pediatrics and its influence on EHR implementation priorities.

Results

Of 164 general acute care children's hospitals, 100 (61%) responded to the survey. Rates of comprehensive (across all pediatric units) adoption ranged from 37% (age-, gender-, and weight-adjusted blood pressure percentiles and immunization contraindication warnings) to 87% (age in appropriate units). Implementation rates for several features varied significantly by children's hospital type. Nearly 60% of hospitals reported having EHRs that do not contain all features essential for high-quality care. A majority of hospitals indicated that the meaningful use program has had no effect on their adoption of pediatric features, while 26% said they have delayed or forgone incorporation of such features because of the program.

Conclusions

Children's hospitals are implementing pediatric-focused features, but a sizable proportion still finds their systems suboptimal for pediatric care. The meaningful use incentive program is failing to promote and in some cases delaying uptake of pediatric-oriented features.

SUBMITTER: Nakamura MM

PROVIDER: S-EPMC8485929 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:Importance:Although the Health Information Technology for Economic and Clinical Health (HITECH) Act has accelerated electronic health record (EHR) adoption since its passage, clinician satisfaction with EHRs remains low, and the association of HITECH with health care information technology (IT) entrepreneurship has remained largely unstudied. Objective:To determine whether the passage of the HITECH Act was associated with an increase in key measures of health care IT entrepreneurship. Design, Setting, and Participants:This economic evaluation of venture capital (VC) activity in the US from 2000 to 2019 examined funding trends in health care IT, EHR-related companies, and all VC investments before and after the passage of HITECH. A difference-in-differences analysis compared investments in health care IT companies with those of companies in 3 categories: general health care (non-IT), IT (non-health care), and all US VC transactions. Data were analyzed from September 2018 to August 2019. Exposures:Venture capital funding received by US companies before and after the HITECH Act. Main Outcomes and Measures:Venture capital investment in health care IT companies and the proportion of those investments going to seed-stage companies, a proxy for very early-stage entrepreneurship and innovation. Results:The data included 70?982 investments, of which 9425 (13.3%) were seed stage, 10?706 (15.1%) were early stage, and 50?851 (71.6%) were growth stage. After passage of the HITECH Act, investment in both health care IT companies and EHR-related companies increased at a rate much faster (13.0% and 11.4%, respectively) than VC as a whole (6.9%). In addition, the proportion of investments going to seed-stage health care IT companies increased compared with both overall VC investments and non-IT health care investments. Health care IT companies saw increased probabilities of transactions being seed-stage of 5.1% (SE, 2.2%; 95% CI, 0.8% to 9.3%; P?=?.02) compared with the entire sample of VC transactions and 13.6% (SE, 1.9%; 95% CI, 9.9% to 17.2%; P?<?.001) compared with non-IT health care VC transactions. Health care IT had essentially 0 increased probability of a transaction being seed stage compared with IT companies outside health care (-0.8% probability; SE, 2.4%; 95% CI, -5.4% to 3.9%; P?=?.75). Conclusions and Relevance:Although widespread clinician dissatisfaction with EHR systems remains a challenge, the HITECH Act's incentive program may have catalyzed early-stage entrepreneurship in health care IT, suggesting an important role for incentives in promoting innovation.

Project description:BackgroundFederal initiatives are underway that provide physicians with financial incentives for meaningful use (MU) of electronic health records (EHRs) and assistance to purchase and implement EHRs.ObjectiveWe sought to examine readiness and interest in MU among primary care physicians and specialists, and identify factors that may affect their readiness to obtain MU incentives.Design/participantsWe analyzed 4 years of data (2008-2011) from the National Ambulatory Medical Care Survey (NAMCS) Electronic Medical Record (EMR) Supplement, an annual cross-sectional nationally representative survey of non-federally employed office-based physicians.Main measuresSurvey-weighted EHR adoption rates, potential to meet selected MU criteria, and self-reported intention to apply for MU incentives. We also examined the association between physician and practice characteristics and readiness for MU.Key resultsThe overall sample consisted of 10,889 respondents, with weighted response rates of 62 % (2008); 74 % (2009); 66 % (2010); and 61 % (2011). Primary care physicians' adoption of EHRs with the potential to meet MU nearly doubled from 2009 to 2011 (18 % to 38 %, p<0.01), and was significantly higher than specialists (19 %) in 2011 (p<0.01). In 2011, half of physicians (52 %) expressed their intention to apply for MU incentives; this did not vary by specialty. Multivariate analyses report that EHR adoption was significantly higher in both 2010 and 2011 compared to 2009, and primary care physicians and physicians working in larger or multi-specialty practices or for HMOs were more likely to adopt EHRs with the potential to meet MU.ConclusionsPhysician EHR adoption rates increased in advance of MU incentive payments. Although interest in MU incentives did not vary by specialty, primary care physicians had significantly higher rates of adopting EHRs with the potential to meet MU. Addressing barriers to EHR adoption, which may vary by specialty, will be important to enhancing coordination of care.

Project description:BackgroundThe Meaningful Use (MU) program has increased the national emphasis on electronic measurement of hospital quality.ObjectiveTo evaluate stroke MU and one VHA stroke electronic clinical quality measure (eCQM) in national VHA data and determine sources of error in using centralized electronic health record (EHR) data.DesignOur study is a retrospective cross-sectional study of stroke quality measure eCQMs vs. chart review in a national EHR. We developed local SQL algorithms to generate the eCQMs, then modified them to run on VHA Central Data Warehouse (CDW) data. eCQM results were generated from CDW data in 2130 ischemic stroke admissions in 11 VHA hospitals. Local and CDW results were compared to chart review.Main measuresWe calculated the raw proportion of matching cases, sensitivity/specificity, and positive/negative predictive values (PPV/NPV) for the numerators and denominators of each eCQM. To assess overall agreement for each eCQM, we calculated a weighted kappa and prevalence-adjusted bias-adjusted kappa statistic for a three-level outcome: ineligible, eligible-passed, or eligible-failed.Key resultsIn five eCQMs, the proportion of matched cases between CDW and chart ranged from 95.4 %-99.7 % (denominators) and 87.7 %-97.9 % (numerators). PPVs tended to be higher (range 96.8 %-100 % in CDW) with NPVs less stable and lower. Prevalence-adjusted bias-adjusted kappas for overall agreement ranged from 0.73-0.95. Common errors included difficulty in identifying: (1) mechanical VTE prophylaxis devices, (2) hospice and other specific discharge disposition, and (3) contraindications to receiving care processes.ConclusionsStroke MU indicators can be relatively accurately generated from existing EHR systems (nearly 90 % match to chart review), but accuracy decreases slightly in central compared to local data sources. To improve stroke MU measure accuracy, EHRs should include standardized data elements for devices, discharge disposition (including hospice and comfort care status), and recording contraindications.