THE EVOLUTION AND DYNAMICS OF ELECTRONIC HEALTH RECORDS SYSTEMS (EHRSS (original) (raw)

2019, Humanities and Social Sciences Review

Abstract

The objective is research to review the transformation process in converting current manual medical records (MMRs) into electronic health record (EHR) systems with emphasis on compelling case studies including one conducted by the author. The study is focused on a select group of countries in North and South America, Oceania, Asia, and Europe. The methodology is based on a review of primarily different governments strategies, and their role in ensuring that the respective systems are successful, without compromising the quality of service. The findings in the study are that the implemented EHR systems are faced with many challenges, despite some subsequent benefits (Delbanco, 2008; Phillips et al., 2009). According to study outcomes, while significant efforts have been made by various governments to encourage EHR systems, enormous challenges (standardization, programming glitches, system failures, vulnerability of patient records, confidentiality, other internal and external factors) continue to slow down the process. There is a global geopolitical absence in instituting an effective and inclusive team to contribute towards the design and development of EHR systems. Inadequate oversight has also impacted implementation processes. Denmark remains a trailblazer in efforts to computerize manual medical records, and could easily be regarded as the "gold standard."

Figures (7)

Figure 1. Hospital Health Record Digitization Rate (%) by Select Countries. Source: C/F Appendix 1, Lainjo 2018

Figure 1. Hospital Health Record Digitization Rate (%) by Select Countries. Source: C/F Appendix 1, Lainjo 2018

Figure 2. Annual Case Load. Source: EP, 2014/ Lainjo B. Annual Service Statistics: Between 2008 and 2011, for which data is available, the total number of client visits peaked in 2009 with a drop in 2010. This drop is associated with extended vacation time taken by the EP for family reasons. The same observation applies to Table 2. Over that period of time, the mean number of patient visits was 6,027 with a corresponding mean number of new patients equal to 445. With respect to insurance claims between 2008 and 2011, there was an approximate average of 3,051 claims for Medicare and a mean of 3,434 for private insurance plans. Figure 2 and Figure 3 represent client load and insurance claim cases respectively. The number of cases and claims in both tables are quite consistent, as expected.

Figure 2. Annual Case Load. Source: EP, 2014/ Lainjo B. Annual Service Statistics: Between 2008 and 2011, for which data is available, the total number of client visits peaked in 2009 with a drop in 2010. This drop is associated with extended vacation time taken by the EP for family reasons. The same observation applies to Table 2. Over that period of time, the mean number of patient visits was 6,027 with a corresponding mean number of new patients equal to 445. With respect to insurance claims between 2008 and 2011, there was an approximate average of 3,051 claims for Medicare and a mean of 3,434 for private insurance plans. Figure 2 and Figure 3 represent client load and insurance claim cases respectively. The number of cases and claims in both tables are quite consistent, as expected.

Figure 3. Annual Insurance Claim by Type. Source: EP 2014/ Lainjo B. Between 2008 and 2011, for which data are available, the total number of client visits peaked in 2009 with a drop in 2010. This drop is associated to an extended vacation time taken by the EP for family reasons.

Figure 3. Annual Insurance Claim by Type. Source: EP 2014/ Lainjo B. Between 2008 and 2011, for which data are available, the total number of client visits peaked in 2009 with a drop in 2010. This drop is associated to an extended vacation time taken by the EP for family reasons.

Figure 4. Annual Morbidity Distribution. Source: EP 2014/ Lainjo B. In terms of morbidity differentials, about 40% of cases diagnosed between 2008 and 2011 were patients with arthritis-rheumatoid. The remaining cases ranged from 3% with Raynaud’s syndrome to 18% with bursitis.

Figure 4. Annual Morbidity Distribution. Source: EP 2014/ Lainjo B. In terms of morbidity differentials, about 40% of cases diagnosed between 2008 and 2011 were patients with arthritis-rheumatoid. The remaining cases ranged from 3% with Raynaud’s syndrome to 18% with bursitis.

Table 1. Computer distribution and setup location

Table 1. Computer distribution and setup location

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