Subject: Technology
Type: Analytical Essay
Pages: 22
Word count: 5550
Topics: Data Analysis, Computer Science, Health, Innovation, Management



The aim of this study is to review the transformation process in converting current manual medical records (MMRs) into electronic health record (EHR) systems. The study also illustrates a case study conducted by the author as stage one of the implementation of the Center for Medicare and Medicaid Services (CMS) electronic health record (EHR) incentive program (Cohen et al., 2015). The third objective is to thematically highlight the prevalence, achievements, challenges and prognosis of implementing EHR systems.


The study is focused on global, regional and national geopolitical systems. A select group of industrial countries in North America, Oceania, Asia, and Europe is used to illustrate the dynamics and transformation system of medical records from manual to electronic. 


The methodology that is used analyzes the global, regional and national implementation rates of the electronic health records systems. The review is made based on different governments, and their role in ensuring that the system is successful, without compromising the quality of service. Physicians’ attitudes towards the system were also used as part of the review process. A comprehensive analysis of the whole EHR systems ecosystem is performed. 


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). The systems are prone to transcribing errors, which the user may not understand nor address (Phillips et al., 2009). These errors may be transferred to a patient’s medical results; a process that is likely to compromise the patient’s safety and quality of service. There is also a likelihood that people using the system may not fully embrace it (Davis & Stoots, 2013). According to study findings, 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 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.”


Innovations come with relative risks. MMRs are no exception, and the challenges are exacerbated by the involvement of different players at various stages of the transformation process. Potential setbacks range from human errors, to computer system breakdowns, to uncontrolled external and internal factors. While caution remains a key mantra, stakeholders (government, doctors, patients, service providers etc.) need to balance the benefits of implementation against the risks of failure and the degree of vulnerability.

According to the research findings, various countries have implementation rates based on the degree of government support and involvement (Davis & Stoots, 2013). For example, in 2009, the U.S had the lowest conversion rates compared to other industrialized countries. This was due to a lack of incentives and encouragement offered by the government to institutions to participate in converting their respective manual systems into electronic ones. This outlook changed in 2012 when the U.S government became more proactive – an initiative that has resulted in an improvement in the conversion rate (Balgorsky, 2014). The case study serves as an illustration of one of the requirements recommended for the successful implementation of the system.


As expected, the implementation of an EHR systems has achieved reducing the bulkiness of paperwork, the safe storage of patient records, and significantly improved access to patient records. The preceding remarks notwithstanding, the likelihood of compromising patient records still remains a major concern despite a lower rate in occurences (Phillips et al., 2009). A compelling complementary and invaluable safeguard is the introduction of strict standardized quality control guidelines. For example, the mitigation of cases where wrong medications with fatal effects are issued to patients will become a non-event. An effective and continuously monitoring framework will go a long way in lessening patient vulnerability. In general, given the currently evolving dynamics, the benefits significantly outweigh the risks, especially in circumstances where all the bugs in the systems have been corrected. While successful implementation is plausible, stakeholders need to be reminded that the provision of operating parallel systems (manual and electronic simultaneously) for a substantial amount of time remains unavoidable. Adapting such a process will guarantee continuity and sustainability.


Literature Review


This section will focus on the main themes raised in the study that impact the implementation of electronic health record (EHR) systems. Despite many health experts acknowledging the benefits linked to an EHR system, it is apparent that the incentives should not outweigh the related challenges. Therefore, this section will analyze the literature review on EHR systems focusing on prevalence of the apparatus, achievements in different health departments, associated challenges, and the prognosis. 

In the 1990s, four out of five doctors in the U.S updated patient records manually and physically stored them in color-coded files. Currently, over 90% of doctors and nurses the country are using EHRs (Gans et al., 2005). Health records continue to change, with most health sectors supporting EHR adoption. In fact, Wolff et al. (2017) believe that healthcare spending on EHRs soared to $3.5 trillion dollars in 2015, up from $2.9 trillion in 2011. In the same year, 2015, medical error costs escalated to $19.5 billion, – increasing to $1 trillion when the accounting processes needed to measure the lost productivity were factored in (Weitzel et al. 2016). Additionally, medical errors represent the third leading cause of death in the US after heart conditions and cancer. By March 2017, over 60% of healthcare providers reported using EHRs, which was a 1% increase in September 2016 (Wolff et al., 2017). The same statistic applied to office-based physicians who had adopted EHRs, an increase of almost double since 2008. 

Currently, there are many prevalent issues raised when it comes to EHR implementation, with the US spending more compared to other countries globally. Quality healthcare generally relies on the integrity, reliability, precise nature, and accuracy of the health information. Adoption of EHRs remains effective in transforming the current healthcare sector into a more efficient, safer, and quality-oriented environment (Vaghefi et al., 2016). However, despite the promises, EHRs have failed to attain projected benefits in health as well as cost savings because of the complicated design and poor implementation of the systems. According to Baro et al. (2015), areas of concern include safety hazards linked to their use, known as “e-iatrogenesis.” The rise of such errors causes loss of data, or incorrect data entry, display, and transmission leading to a loss of integrity of the information. 

Unfortunately, there is little literature providing evidence that could quantify the magnitude of EHR-associated risks, yet the tools have become part of the health delivery system, exposing patients to induced medical errors, and even death, which have continued to increase (Jha et al., 2008). Currently, there are no clear regulatory requirements to safeguard the implementation of the tools, with available policies, and usability best practices not widely known to developers, or users in relation to product functioning. 

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Current EHR MS

Activation of EHRs require a multi-disciplinary methodology that could assure privacy and security, and effective design practice workflows. According to Anderson (2007), it is also significant to train the team and have proper management systems to support adoption. Every health institution must have a unique EHR implementation that could aid in the adaption process of a new system that benefits patients, doctors and other staff. Any current EHR MS project requires the institution to form an implementation team including nurses, receptionists, compliance and administration staff, as well as medical assistants. The clinical staff plays a significant role in training other teams and challenging the implementation team to do the right thing (Charles, Gabriel & Furukawa, 2013). The significant roles identified include a lead physician, a lead user, and the project manager. 

The first stage entails configuring software to meet set security measures, in accordance to the HIPAA risk assessment. The next step is identifying the hardware needs, followed by transferring data and optimizing the pre-launch workflows (Ludwick & Doucette, 2009). It is also essential to prepare the room layout since the section where the computer is placed affects patient care. Another consideration includes the launch agreement, which could be a “big bang” launch or an incremental one. After agreeing on the launch approach, affected employees should begin the acclamation of the new system and the implementation of developed strategies. 

It is obvious that every EHR system could occasionally be down due to power outages or unexpected malfunctions. The concerned parties need to have clear procedures in place when this occurs, providing clear guidelines on how to handle workflows. Every EHR system requires continous training among employees to ensure that they understand its implementation (Fernandez-Aleman et al., 2013). 

Routine and Ad Hoc Reporting

According to Charles, Gabriel, and Furukawa (2013) numerous medical offices had already computerized their practice management (PM) applications, even if the office maintained paper records, used EHR systems or adopted a hybrid system. PM systems remain prevalent because of the increased instances of claims submission, as well as adjudication. Lack of an efficient electronic application is costly and slows down mail. Records by American Medical Association show inefficient claims submission applications amounted to $210 billion yearly (Baro et al. 2015). However, an effective PM system helps employees capture data when they encounter patients, aiding in the reimbursement of services provided. The functions a PM systems performs include generating claims for reimbursement, helping apply payments or even denials, as well as generating patient statements in the case a patient has a payment balance. According to Buck et al. (2010), PM systems perfect the generation of business correspondence. Accordingly, PM systems develop databases for healthcare professionals to practice their careers, payers to pay hospital bills, determining patient demographics and recording patient encounters. Some of the recorded elements include date, codes, charged bills, paid amounts, billing messages, and place (Blumenthal, 2010). 

Particularly, the focus on PM systems in EHR implementation includes offering routine, as well as ad hoc reports, whichs allow administrators to analyze trends for specific practices and the implementation of performance improvement strategies relying on provided findings (Hayrinen, Saranto, & Nykanen, 2008). An example includes a medical administrator using a PM application to compare various payers in relation to reimbursed amounts for every service provided, and turnaround time for claims submissions, as well as payments (Chiasson et al. 2007). The results guide the decision-making process of managing care plans and adopting the most effective ones, while disregarding ineffective ones. Additionally, the system analyzes payers of a given service, establishing if the health practice was a perfect clinical time. Such analysis can help establish if a practice such as managing a patient from home directly or through an agency should be continued or abandoned. 

First, an administrator has to measure unprofitable services that can have a negative effect on custom service provision, with the PM application providing a solution to measure payment performance. Other capacities include patient scheduling software that increase business efficiency in relation to medical practice. In fact, some provide an encoder that helps in the selection and sequence of correct diagnoses and health procedures (Daglish & Archer, 2009). The physicians could establish proper codes using superbill, which represent common codes utilized in practice alongside the charged amount for every procedure. In some instances, diagnosis procedures are not provided on a superbill, therefore the encoder allows for a search of main terms, as well as the selection of the best code. Additionally, other encoders are packaged together with tools that include newsletter publication to AMA, helping physicians subscribe to the right coding initiatives leading to reimbursement optimization (Wolff et al. 2017). This helps in reimbursement in a legal and ethical practice since it assist in elimination of fraud and other fines due to improper coding. 

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Clinical and Administration of Work Flow Procedures

Medical practice involves common medical procedures used to treat patients and offers proper reimbursement for provided services. The procedures division relies on whether the patient is a new or repeat patient. The first procedure includes the registration of a patient via the practice’s website or through the phone. The next step entails patient scheduling and confirmation of appointments. If the patient is new, the administrator has to verify insurance information to understand the level of the insurance plan and the covered services. The next procedure requires patient check-in with the PM system integrated to the EHR system, allowing for the scan of documents, such as bubble sheets the patient completed during registration. The next process is the clinical encounter starting with the nurse taking vitals and collecting blood or urine samples from the patient if necessary, in addition to updating subjective history. The physician then examines the patient and records additional historical information, in addition to finishing the physical exam and updating the notes in a SOAP order: subjective, objective, assessment, and plan. Most physicians use a paper system to dictate the process, with some using voice technology to dictate to a device, and print out a report from the software for filing. 

However, with an EHR application, the physician has different options to record patient information in a clinical record. EHRs provide for voice recognition software, templates, as well as standard dictation. This means that when a doctor has a face-to-face encounter with a patient, EHR encounter by a nurse has already begun by recording a patient’s complaints, vital signs or updates related to the subjective history under the SOAP procedure. Similarly, a physician continues to build encounter notes using drop-down menus to show body systems examined, tests performed, prescriptions ordered, assessments, as well as plans. 

Every selection made by a physician is added to clinical notes, which is a perfect example of data maintained through the EHR system but cannot be shared to a PM application. Nevertheless, EHRs using “computer assisted coding” (CAC) technology convert standardized notes to codes, used in both EHR and PM systems. According to Vanek et al. (2016), he indicates that many EHR run office notes using logic to assign CPT evaluation and management codes. It uses 1995 and 1996 guidelines, with EHR application passing the codes as well as ICD-XX-CM codes over to an integrated application from same vendor of interfaced among different vendors (Baro et al. 2015). Physicians then conclude the clinical elements of an encounter with a decision to discharge patients, and provide follow-up instructions and literature to educate the patient. When samples get to the lab and a patient requires a prescription, the doctor uses e-prescribing to do so, which is then sent back to the pharmacy electronically, and in some instances, the patient obtains a paper copy. 

During the patient checkout, a patient receives a discharge, and the receptionist collects any payment due, and notes schedules for follow-up visits. While using a standalone PM system, the administrator enters the charges from the superbill, but in some instances, the services are coded using information. When using an integrated system of a PM and EHRs, then the coding is very beneficial. However, the employee responsible for the coding has to verify applicable codes brought over from the PM application. Furthermore, the claims-bill submissions are electronically transferred and sent through cycles. After updating the PM application, the claim joins the queue awaiting transmission to the payer. The last two stages entail payment posting and reporting. During payment posting, money is deposited in a practice account, with the payer mailing a paper copy to every person with benefit explanations for each. Billing personnel follow-up in the case of a patient having more than one payer, to establish if the transmission was done to the right secondary payer. Lastly, daily routine and ad-hoc reports are always run and verified to match deposits. 

EHR Adoption

Outpatient EHR Adoption

The 2006 adoption rate of outpatient (also known as ambulatory) EHRs ranged between 1-20%, depending on the study read and group under study (Jha et al., 2008). Most quoted statistics were obtained from surveys but had their own shortcomings. Most outpatient practices have EHRs but continue running dual systems of paper and electronic. Another concern includes small or rural practices that lack essential finances and technology to implement EHRs. In 2008, Jha et al. wrote a seminal article on the adoption of outpatient EHRs, with a sample of 5,000 physicians selected from an AMA file. 

The insightful findings reported only 4% of those surveyed used a purely EHR system for order entry and decision support. In addition, only 13% used the basic EHR, with earlier reports showing that the adoption rate was high for medical centers (Jha et al. 2008). Blumenthal (2010) explained that the adoption rate remained high for huge medical groups and centers, with responding physicians indicating multiple beneficial effects associated with the use of EHRs. Accordingly, National Ambulatory Medical Care conducted a survey in 2012, which showed that 72% of office-based respondents have EHRs, compared to about 48% reported in the previous year. Despite the numbers recorded by various studies, varying in percentages depending on states, it is clear that EHRs adoption is high. 

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Inpatient EHR adoption

Lodwick and Doucette (2009) study findings showed that respondents used the basic EHR  system, with only 1.5% using the comprehensive one. Notably, only large urban health centers and academic centers reported high adoption rates. In comparison, according to Hillestad et al. (2005), rural and small health institutions have reduced adoption rates of EHRs. However, most studies failed to report the user satisfaction rate (Lodwick & Doucette, 2009; Hsiao & Hing, 2012; Huryk, 201; Buck et al. 2011). 

Current Geopolitical Dynamics

Until recently, US EHR adoption rates were lower compared to other developed countries. According to Chiasson et al. (2007), in 2006, the US lagged behind other industrialized countries in adopting EHRs. Even in 2009, the country still lagged behind, particularly among primary care doctors (Shachak & Rei, 2009). The adoption rate was still low in other developed countries globally. However, things began to change in 2012 all over the world with the US experiencing an increase from 46% to 69%, while Canada recorded an increase from 37% to 56%. Nevertheless, most of the adoptions were mainly basic, with multi-functional adoption still lower, specifically among small medical institutions (Vanek et al. 2016).

According to Fernandez-Aleman et al. (2013), the main difference between the US and other high EHR adopter countries is the level of government involvement. Other developed countries received intensive support from federal and state governments. An example includes the UK, whose population equals 20% of the US’s, investing over $17 billion through their National Program for IT (NPfIT). On the other hand, Australia has given physicians adopting EHRs subsidies managed through the National E-Health Transition Authority (NEHTA) (Charles, Gabriel & Furukawa, 2013). Additionally, Germany has established a public-private partnership to promote interoperability standards and certification of EHR systems, known as Gematik. The same applies to Denmark, which is currently the international leader in adopting IT into its healthcare systems. Denmark has the highest EHR adoption rate, with the most interoperable ones globally. Despite the impressive figures, countries such as the UK have been facing increased challenges, which forced the nation to dismantle their $17 billion Health IT development projects. The UK categorically stated that their main vendor, Computer Sciences Corporation, had wasted over $10 billion because it never provided the expected software. To help it catch up with countries such as Denmark, the US has adopted a HITECH Act (209) that provides EHR incentive programs. 


Using Analytics and Cloud Computing in Managing EHRs

Healthcare analytics, a term used to describe analysis activities on collected data in various areas of healthcare can benefit hospitals greatly if there is an effort made to understand the discoveries that emerge, as opposed to simply focusing on straight facts. Analytics is useful when managing the EHRs since it reduces administrative cost and can help determine how care can be improved. Through analytics, hospital systems can properly make use of and exchange information that already exists in their systems. This is achieved by ensuring that medical codes are used properly, and therefore, a correct refund is received (Gupita, 2016).

Analytics can also help to reduce abuse and fraud (Gupita, 2016), which costs healthcare institutions a significant amount of money. It is therefore important for hospitals and healthcare  institutions to use analytics to protect the patient’s information, including what procedures physicians are performing on their patients. Other benefits of using analytics include tracking down incorrect payments, and allowing healthcare institutions to keep track of patients’ lifestyle behaviors (Gensinger, 2014).

Cloud computing uses the latest technology to access, deploy, and use networked applications, resources and information. It is made up of a complex infrastructure that is difficult for some people to understand. Cloud is able to transform healthcare by providing institutions with software and on-demand hosted services. It can also deploy applications that offer a security-enhanced environment for web services. However, in the use of cloud computing services, a strategy must be applied (Moumtzolglou & Kastania, 2014). A cloud strategy can be used to allow public access to medical resources. This computing service is invaluable when a patient’s information needs to be shared among medical providers, especially in the case of an emergency (Au et al., 2017). 

A public cloud can not only be used to share information with patients, it can also connect physicians and transfer electronic documents. Examples include: nonclinical healthcare management clinical, health management, and nonclinical. These facilities can be able to share patient’s information among themselves when the patient is referred to these facilities for further treatment. Whether managed externally or internally in the data center, important features of these infrastructures are improved security and privacy when applying a public cloud plan. Although there are risks connected to data security in a private cloud, certain safeguard measures should be taken, including the use of a virtual private network, which can address possible security risks when there is a limited access to the cloud (Mueller et al., 2016).

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Challenges that face the assessment of EHR usability include the complexity of the system interaction with a complete socio-technical framework in which it is normally used, difficulties in measuring the influence of the system to the downstream process, the intended user and the professional role the system intends to the play (Kannry, 2011). Usability in healthcare settings is particularly difficult since the a software is designed to capture the needs of many diverse users who have different geographic work environments, varying requirements, cultural boundaries and maybe participating in the product design (Smith, 2009). A lot of health IT effects are only considered as “emergent” because they are only discovered after the system has been monitored after use. The metrics identified for usability are normally subjective user satisfaction and time on task. Multiple measures for these metrics are required, and are useful to the users and developers (Smith, 2009).

It has been suggested that the EHR system introduction has led to an increase of faulty data recorded, instead of improving the data quality being recorded (Zelicoff & Bellomo, 2008). Since the EHR system’s main goal is the reduction of medical errors, new reports of new types of errors compromising the patient’s safety have emerged. For instant, cancer treatment for a patient was initially delayed by a couple of years, due to an error in the physician’s EHR system, which showed old normal Pap smear test results instead of more recent abnormal Pap smear results (Ledue, 2010). In a different case, a baby died from a drug overdose because of a transcription error, where a manual handwritten request was entered into the computer. The medical error could have been prevented if automated alerts was activated (Harman, 2006).

Since the safety monitoring of EHRs does not have regulatory framework, these systems may have programming errors or viruses, been developed from incomplete design specifications, and changed the clinicians daily routines, etc. The increasing complexity and scope of roles for clinicians using these systems, along with the pressure to adapt to the new system, increases the chances of EHR-associated patient safety hazards. In a complicated healthcare environment, where interactions with numerous computer systems have an effect on the function of the system, it is a challenge for the user to anticipate potential constrains or understand how an error occurred. In addition, when the providers have already invested money in the training and implementation of the system, they may retain the system to avoid the high cost of replacement, even after discovering the system is flawed (Kannampallil & Kaufman 2017).


Health Information Technology (HIT) literature has shown significant efficiency and quality related drawbacks as well as benefits. Health IT is positively changing healthcare delivery and has highly penetrated the main health systems with different efficiency levels. Various healthcare procedures are communication and information based and rely on different levels of technologies (Walker et al., 2005). When the stakeholder’s adoption of these health information technologies is not well planned, the results are the development of fragmented or partial solutions. This is not sufficient for the national implementation of EHR systems. A nationwide approach is essential to have integrated collaboration and action between IT and healthcare. While some EHR users have adopted the user-centered design during development, the culture of practice is not universal. This may make it difficult to apply to the systems legacy. This perspective suggests that the use of various techniques is followed by variable efficacy (Amatayukul et al., 2007). Studies show that implementation of a multifunctional system may yield real benefits in terms of enhanced monitoring, surveillance activities, medical error reduction and improved delivery of care-based guidelines.

EHR System Design Flaws

The EHR system expanding capabilities require very complex software, which increases the likelihood of failures in the software which may result in harm to patients.  Flawed software, such as a glitch that contains inaccuracies in hundreds or thousands of patient records contributes to patient vulnerability. (Phillips et al., 2009). Software viruses may delete information, jumble data or deposit the data in the wrong place. Computers may also spew forth a number of disorganized data in such a way that the physician is unable to find critical patient information quickly. Data can also be corrupted or missing. For example, with laboratory results, the value may be sent to the physician with an extra inadvertently inserted character. Problems in the system interface may lead to delays, errors, data loss, system downtime and unnecessary testing (Harrison et al., 2007).

Improper Use and Poor System Usability

Errors can result from improper use of the system. Errors in usability occur due to lack of user-friendly interfaces, system complexity limitation of the user and work flow incompatibility (Ledue, 2010). A faulty functionality or interface can mislead the clinicians. This is due to incorrect values originating from programming errors. A typical case is the inaccurate conversion from one measurement to another, such as Fahrenheit to Celsius or kilograms to pounds. There is a new kind of error that is common but does not affect the paper-based system. This is referred to as the ‘adjacency error,’ where the user selects an item in a drop-down menu, which is next to the intended one. This can result in supplying the wrong medication to a patient (Singer, 2010).

Clinicians share control of complex processes in computers. They may assume a high-level role and give way for the computers to make decisions and perform appropriate actions. For example, the computer can automatically generate a laboratory order in case a certain medication has been ordered. Even though the EHR system does not directly affect the care of the patient without human involvement, this form of technology is normally complicated and risky. This is because users are unable to understand or analyze the computations, and hence, cannot effectively intervene (Weiskopf et al., 2013). For example, the clinicians may depend on computer-generated treatment and diagnosis recommendations without understanding how the development of algorithm was designed. They may also not understand that the algorithm doesn’t take into account clinical factors or certain medical conditions that are relevant to the patient (Singer, 2010).

Workarounds are normally exercises by users during the time that the systems are not sufficiently flexible to support real-life workflow patterns and clinical practice (Sittig & Hardeep, 2011). These workflows can further demoralize the patient. For example, sometimes the system cannot permit drug administration until the order has been recorded in the system by the clinician. Therefore in this case, documentation of the order can only occur after administering the drug. This could result in the drugs being administered again, which can be fatal to the patient. Also, disabling alerts because they are distracting can result in harm to the patient because the critical safety feature is not being applied when needed (Sittig & Hardeep, 2011).

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Copy and Paste

The growing trend of “copy and paste,” also referred to as cloning, is a significant problem in EHRs and is worsening since it continues to increase. The process involves copying a text from a previous section to another page. Documentation integrity risks that result from the incorrect use of copy and paste functionality include: incorporation of incorrect information, inability to identify the purpose or the author of the documentation, inaccurate information and unnecessary long progress notes. The overall integrity of the health record is spoilt, and therefore there is the possibility of harming a patient (Kabene, 2010).

The ease that comes with copy and paste has resulted in clinicians complaining that EHRs are normally cluttered with irrelevant information, making it hard to locate important details and to read the record (IRMA, 2011). When the EHR becomes a large warehouse of irrelevant, disorganized or erroneous data, the patient’s illness is no longer easy to diagnose, which affects clinical decision-making and can contribute to medical malpractice (IRMA, 2011). A critical care medicine study, published in 2013, discovered that 82% of progress notes from residents and 74% percent of physician attending notes had 20% or more copied texts. Also a journal of the American Medical Informatics Association (AHIMA) reported that 54% of progress notes and 78% of sign-out notes had copy-pasted text. Text that has been improperly copy-pasted may not be easily detected (AHIMA, 2012).


EHR systems can transform and change the way the healthcare is delivered to patients when these advanced technologies are designed, implemented and used properly. The prevalence of use is highest in Denmark and lowest in developing countries (Sicillia & Balazota, 2013). The system has its shortcomings and benefits. For example, the system has resulted in a good elimination rate for paperwork, which is bulky to store and can also lead to misplacement of patient records. Along with the possibility of compromising patient safety if designed improperly, this system initially adds layers of problems in the healthcare delivery set up. These problems can lead to unintended and harmful consequences, such as failure to detect serious illnesses, dosing errors and treatment delays because of poor human-computer interactions or data loss. 

Little has been done to measure systematically, identify root causes, analyze the risks and implement strategies to mitigate these concerns. In addition to the safety risks and quality of care, EHR-associated errors may act as a barrier to the system’s adoption and use. Also, there has been a trend of copy-pasting, where the user copies a text from one site to another. The text pasted may lack some vital patient information, hence misleading the clinician about the patient’s status. System conversion errors are common and can lead to accessing misleading patient information. 

Many policy makers tend to believe that EHR risks are minor and easily manageable, but this is not the case. Patient safety and quality of care can be compromised by improper EHR use or design. The failure to address the problem of information integrity in these systems will lead to the increase, instead of the desired decrease of medical errors and healthcare costs. A joint team of inclusive and informed stakeholders and the federal government (as in the US and elsewhere) is vital in establishing necessary and effective oversight. Such a team would help to prevent potential unintended consequences. Federal government leadership in developing and implementing guidelines including enforcement, and the development of high national performance standards will contribute significantly in reducing serious EHR-related errors.

Federal governments alone cannot eliminate EHR-related adverse effects. The system vendors and developers need to adopt usability and design standards in an effort to improve system safety and integrity of the information. The healthcare providers also need to develop and apply procedures and policies that strengthen proper EHR system use. This would serve as one strategy to mitigate server system errors.   

If proper measures are established for the EHR system, there is a future for this system. This is because most of the drawbacks related to this system can be corrected. When addressed properly, the stakeholders will then realize that the benefits of an efficient and effective EHR system outweigh the risks. Adoption and implementation of these revisions by the relevant stakeholders is bound to motivate and increase the level of interest from service providers, and as a result increase the implementation rate of EHR systems.

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  1. American Health Information Management Association (AHIMA). (2012). Data quality management model. Chicago.
  2. Amatayakul, M., Lazarus, S. S., & Medical Group Management Association. (2007). Electronic health records: Transforming your medical practice. Englewood, Co: Medical Group Management Association.
  3. Anderson, J. G. (2007). Social, ethical and legal barriers to e-health. International journal of medical informatics76(5), 480-483.
  4. Au, M. H. A., Castiglione, A., Choo, K.-K. R., Palmieri, F., & Li, K.-C. (2017). Green, Pervasive, and Cloud Computing: 12th International Conference, GPC 2017, Cetara, Italy, May 11-14, 2017, Proceedings. Pg 612-620.
  5. Balgorsky, B. J. A. (2014). Essentials of Health Information Systems and Technology. Sudbury: Jones & Barlett publishers.
  6. Baro, E., Degoul, S., Beuscart, R., & Chazard, E. (2015). Toward a literature-driven definition of big data in healthcare. BioMed research international2015.
  7. Blumenthal, D. (2010). Launching hitech. N Engl J Med2010(362), 382-385.
  8. Buck, M. D., Anane, S., Taverna, J., Amirfar, S., Stubbs-Dame, R., & Singer, J. (2011). The Hub Population Health System: distributed ad hoc queries and alerts. Journal of the American Medical Informatics Association19(e1), e46-e50.
  9. Charles, D., Gabriel, M., & Furukawa, M. F. (2013). Adoption of electronic health record systems among US non-federal acute care hospitals: 2008–2013. ONC data brief9, 1-9.
  10. Chiasson, M., Reddy, M., Kaplan, B., & Davidson, E. (2007). Expanding multi-disciplinary approaches to healthcare information technologies: what does information systems offer medical informatics?. International journal of medical informatics76, S89-S97.
  11. Cohen, A. B., Colby, D. C., Walloo, K., & Zelizer, J. E. (2015). Medicare and Medicaid at 50: America’s entitlement programs in the age of affordable care.
  12. Daglish, D., & Archer, N. (2009, August). Electronic personal health record systems: a brief review of privacy, security, and architectural issues. In Privacy, Security, Trust and the Management of e-Business, 2009. CONGRESS’09. World Congress on (pp. 110-120).
  13. Davis, C., & Stoots, M. (2013). A guide of HER adoption: Implementation through organizational transformation. Chicago, IL: healthcare information and management systems society.
  14. Delbanco, T., Sands DZ. (2008). E-mail between doctors and patients: Electrons in flight. N Engl J Med. 350:1705-1707.
  15. Fernández-Alemán, J. L., Señor, I. C., Lozoya, P. Á. O., & Toval, A. (2013). Security and privacy in electronic health records: A systematic literature review. Journal of biomedical informatics46(3), 541-562.
  16. Gans, D., Kralewski, J., Hammons, T., & Dowd, B. (2007). Medical groups’ adoption of electronic health records and information systems. Health affairs24(5), 1323-1333.
  17. In Gensinger, R. A. (2014). Analytics in healthcare: An introduction. Chicago, IL: Healthcare Information and Management Systems Society. Pg 47-50.
  18. Gupta, A. (2016). Advances in Healthcare Informatics and Analytics. Springer International Publishing.
  19. Harman, L. B., & American Health Information Management Association. (2006). Ethical challenges in the management of health information. Sudbury, Mass: Jones and Bartlett Publishers.
  20. Harrison, Michael I., Ross Koppel, and Shirly Bar-Lev. “Unintended Consequences of Information Technologies in Health Care—An Interactive Sociotechnical Analysis.” Journal of the American Medical Informatics Association 14 (2007): 542.
  21. Häyrinen, K., Saranto, K., & Nykänen, P. (2008). Definition, structure, content, use and impacts of electronic health records: a review of the research literature. International journal of medical informatics77(5), 291-304.
  22. Hillestad, R., Bigelow, J., Bower, A., Girosi, F., Meili, R., Scoville, R., & Taylor, R. (2005). Can electronic medical record systems transform health care? Potential health benefits, savings, and costs. Health affairs24(5), 1103-1117.
  23. Hsiao, C. J., & Hing, E. (2012). Use and Characteristics of Electronic Health Record Systems Among Office-Based Physician Practices, United States, 2001-2012.
  24. Huryk, L. A. (2010). Factors influencing nurses’ attitudes towards healthcare information technology. Journal of Nursing Management18(5), 606-612.
  25. Information Resources Management Association. (2011). Clinical technologies: Concepts, methodologies, tools and applications. Hershey, PA: Medical Information Science Reference. Pg 160-167.
  26. Jha, A. K., Doolan, D., Grandt, D., Scott, T., & Bates, D. W. (2008). The use of health information technology in seven nations. International journal of medical informatics77(12), 848-854.
  27. Kabene, S. M. (2010). Healthcare and the effect of technology: Developments, challenges and advancements. Hershey, PA: Medical Information Science Reference. Pg 75-82.
  28. Kannampallil, T. G., & In Kaufman, D. R. (2015). Cognitive informatics for biomedicine: Human computer interaction in healthcare. Pg 197-216.
  29. Kannry, Joseph. (2011). Effects of E-prescribing systems on patient safety. Mount Sinai journal of Medicine. 830.
  30. Ledue, C. (2010). Medical errors cost U.S. economy almost $20 billion.
  31. Ludwick, D. A., & Doucette, J. (2009). Adopting electronic medical records in primary care: lessons learned from health information systems implementation experience in seven countries. International journal of medical informatics78(1), 22-31.
  32. Moumtzoglou, A., & In Kastania, A. (2014). Cloud computing applications for quality health care delivery.
  33. SSCC (Conference), In Muller, P., In Thampi, S. M., In Alam, B. M. Z., In Ko, R., In Doss, R., & In Alcaraz, C. J. M. (2016). Security in computing and communications: 4th International Symposium, SSCC 2016, Jaipur, India, September 21-24, 2016, Proceedings. Pg 615-617.
  34. Phillips Win., David Fleming. (2009). Ethical concerns in the use of electronic medical records. 20-31.
  35. Shachak, A., & Reis, S. (2009). The impact of electronic medical records on patient–doctor communication during consultation: a narrative literature review. Journal of evaluation in clinical practice15(4), 641-649.
  36. In Sicilia, M. A. & Balazota, P. S. (2013). Interoperability in healthcare information system: Standards, management , and technology.
  37. Singer, Stacey. “Electronic Medical Records May Cause Patient Care Errors, Florida Medical Board Says.” Palm Beach Post News, June 5, 2010. 
  38. Sittig, Dean F., and Hardeep Singh. “Defining Health Information Technology-Related Errors.” Archives of Internal Medicine 171 (2011): 1281.
  39. Smith, P. (2009). Performance measurement for health system improvement: Experiences, challenges, and prospects. Cambridge: Cambridge University Press. Pg 577-581.
  40. Vaghefi, I., Hughes, J. B., Law, S., Lortie, M., Leaver, C., & Lapointe, L. (2016). Understanding the impact of electronic medical record use on practice-based population health management: a mixed-method study. JMIR medical informatics, 4(2).
  41. Vanek, V. W., Ayers, P., Charney, P., Kraft, M., Mitchell, R., Plogsted, S., … & Kent, S. (2016). Follow-up survey on functionality of nutrition documentation and ordering nutrition therapy in currently available electronic health record systems. Nutrition in Clinical Practice31(3), 401-415.
  42. Walker, J. M., In Walker, J. M., In Bieber, E. J., & In Richards, F. (2005). Implementing an electronic health record system. London: Springer.
  43. Weiskopf, Nicole Gray, and Chunhua Weng. “Methods and Dimensions of Electronic Health Record Data Quality Assessment: Enabling Reuse for Clinical Research.” Journal of the American Medical Informatics Association 20 (2013): 144.
  44. Wolff, M., Wells, B., Ventura-DiPersia, C., Renson, A., & Grov, C. (2017). Measuring sexual orientation: A review and critique of US data collection efforts and Implications for health policy. The Journal of Sex Research54(4-5), 507-531.
  45. Zelicoff, A. P., & Bellomo, M. (2008). More harm than good: What your doctor may not tell you about common treatments and procedures. New York: Amacom American Management Association.
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