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These systems were categorized by currently receiving, preparing to receive, or not receiving no system, state-run system, or do not know data for meaningful use. Since clinical services are not essential services provided by LHDs, LHDs with no clinical services were not included in this question provided by the Informatics Capacity and Needs Assessment Survey. The independent variables considered for the logistic regression model included LHD characteristics theoretically associated with informatics capacity.

Variables included infrastructural and organizational activities, training, and control of IT decisions. Variables regarding infrastructure of LHDs include a 5-level population size 1. The variables representing the organizational activities performed in the past 2 years are reviewed current systems, created a strategic plan for information systems, used formal project management process, formally conducted a readiness assessment, and had the provision of training within the last 12 months.

The variables for control of IT decisions were represented by hardware allocation and acquisition, software selection, software support, and IT budget allocation. In addition, the control variables included decisions within each department or program, within LHD through central department , through city or county IT department, through state agency, or through someone else. The selection of the variables was made prior to analyses and was based on review of the literature. However, our variable selection was limited by the availability of the Informatics Capacity and Needs Assessment Survey.

The variable selection was also conditioned by elimination of some variables highly correlated with each other. To avoid multicollinearity, we excluded only those independent variables that were not highly correlated with each other. Our final selection of independent variables had the Pearson correlation coefficient of 0. Descriptive analysis was performed to compute frequencies and percentages for the meaningful use and independent variables.

To avoid small cell counts, 2 separate logistic regression models were computed—one to model the organizational characteristics and the other for IT characteristics.

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The Nagelkerke pseudo- R 2 for the first model Table 1 was 0. More than half of LHDs were not receiving data from certified EHRs for meaningful use from syndromic surveillance systems, cancer registries, and other specialized registries. Eighty-seven of the LHDs had less than 25 population jurisdiction, followed by 79 from to population size. After controlling for other independent variables in the model, LHD characteristics statistically associated with having an EHR system were having local governance.

The results indicate an inverse relationship between EHR system implementation and having a strategic plan related to informatics in the past 2 years Table 1.

LHDs that provided informatics training in the past 12 months had twice the odds of using an EHR system compared with those who did not conduct informatics training. Table 2 includes the type of IT control and the department, agency, or outside agency that controls it. LHD characteristics statistically associated with having an EHR system were having local governance, not having created a strategic plan for information systems in past 2 years, had informatics training in the past 12 months, and had various agencies and department of controlling IT decision making of hardware allocation or acquisition, software selection, software support, and IT budget allocation.

The findings indicate the most commonly received certified EHR data for meaningful use stage 1 was electronic laboratory reporting and immunization information systems. Reaching meaningful use has many implications for LHDs, such as providing real-time and relevant data for patient care, improving in patient health outcomes, and driving action for change in population health. The LHD population size can affect the resources and infrastructure of the LHD, which have an impact on the implementation of EHRs due to the economies of scale and scope.

The workforce within the LHD is also affected by increased productivity and a wider range of IT skill sets. The findings indicate that LHDs with state governance tend to have EHR systems more often than local or shared governance. State health departments have different budget allowances, funding streams, and workforce that greatly influence the capabilities of IT infrastructures. Strategic alignment is a key characteristic in the implementation of LHD informatics.

Implementing electronic health records in hospitals: a systematic literature review

This study illuminated that the LHDs that have not created a strategic plan for information systems within the past 2 years are more likely to have EHRs. However, since this study did not consider strategic plans created outside of the past 2 years nor the date of EHR implementation, it cannot be conclusively determined that there was an absence of a strategic plan related to informatics just prior to the 2 years. Our study has some limitations related to biases due to type of respondents and self-reporting of informatics capacities.

Before administering the survey that is the source of data for this study, the project team asked the contact persons for the LHDs in the study sample to identify the most relevant informatics staff. Only a quarter of the LHDs provided the informatics staff contacts, resulting in mixed perspective of LHD informatics and the leadership staff. Also, the self-reported survey responses were not independently verified. Public health agencies, including LHDs, need increased health IT capacities to provide the core public health functions efficiently and effectively.

As mentioned in the literature, societal outcomes could be the ability to provide holistic and comprehensive care to individuals and populations served by LHDs. This study shows that LHD characteristics that have shown positive influences on implementation of EHRs regardless of population size include reviewing some or all of the current information systems to determine whether they needed to be improved or replaced, conducting formal readiness assessment for health information exchange, providing informatics training, and conducting analysis of control levels of decisions of hardware allocation or acquisition, software selection, software support, and IT budget allocation.


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The Future of Electronic Health Records: 6 Predictions | Institute for Informatics

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Epic: Electronic Health Records

Williams, Karmen S. The authors declare no conflicts of interest. Back to Top Article Outline. TABLE 1. TABLE 2. A ride in the time machine: information management capabilities health departments will need.

Am J Public Health. Cited Here Ben-Assuli O. Electronic health records , adoption, quality of care, legal and privacy issues and their implementation in emergency departments.

Health Policy. Factors associated with patient trust in electronic health records used in primary care settings. Health Policy Technol. Strudwic G, Eyasu T. Electronic health record use by nurses in mental health settings: a literature review. Arch Psychiatr Nurs.