Optimizing health care delivery by adapting diagnostics in a low-resource setting: The case of San Miguel Hospital, Sucumbíos, Ecuador.

Predictive value of rapid influenza tests varies with prevalence

This month, Steven Shafer, Editor-in-Chief of Anesthesia and Analgesia, sets out that journal’s policy on ethical approval and informed consent in an editorial [1] that is reproduced, in whole, in this issue of Anaesthesia [2] (in order to demonstrate first, how journals can cooperate and second, how Editors-in-Chief can agree to disagree). My invited response – you’re reading it now – is similarly reproduced in Anesthesia and Analgesia, so that readers of both journals can see and join in the dialogue. I am grateful to Dr Shafer and to both publishers for the opportunity and permission to respond. Dr Shafer opens by stating Anesthesia and Analgesia’s requirement for ethical approval and informed consent for research, with which I certainly agree; indeed, it’s a central requirement for this journal too [3]. He then goes on to raise the question: What about audits and case reports? I have addressed this question before in this journal, and Dr Shafer questions my statement that ‘research tells us what works best and audit tells us whether we’re doing it or not’ [4], going on to claim that my distinction is ‘irrelevant’. I both disagree and agree with his claim, as I will attempt to explain below. First, I stand by my definition. If we want to know whether Drug A works better than Drug B for condition X, the process we use is research: we design a study – in most cases, a randomised controlled trial – and from the results, draw conclusions about the relative merits of the two drugs (for the sake of argument, let us say that Drug B is best. And let’s also say that the evidence for this finding is overwhelming and not in doubt). This description meets the criteria for research outlined by Dr Shafer in his reference to dictionaries and the US Department. Audit, on the other hand, is a process by which we ensure that patients with condition X actually do receive the better treatment – Drug B – rather than Drug A. A randomised controlled trial has no place here; we need to look at patients with condition X, either locally or nationally, and see what they are prescribed. We then compare our findings (e.g. that only 50% of such pati'ents do indeed receive Drug B) with our standard of care (this may be a local standard or a national one. Let’s say that the Association of Condition X-ologists has decreed that 90% of sufferers should receive Drug B), and then look at how this sorry state of affairs might be improved. After appropriate intervention, be it education, improved availability of Drug B, better protocols, etc., we repeat the audit in the hope that the figure has increased from the original 50% (‘closing the loop’). The two processes are clearly different, although they both include data collection, analysis and interpretation. Second, I agree with Dr Shafer in that this definition is not useful (which was my point in giving the definition in the first place [4]). The problem is that not all research is so clearly demarcated by a randomised controlled trial, and not all audit fits so well into the ‘audit cycle’ described above. Research is not only about whether one treatment works better than another; it is also about how a treatment works, how a disease process develops, etc. And take, for example, condition Y, which has only just been described; in order to see how sufferers are being treated, we need to collect data in exactly the same way as for condition X, but without the comparison against a standard (for there isn’t one yet). Many would call this a ‘survey’– or even ‘research’; some would still label it an ‘audit’, albeit the first part of one. In the UK, we have additional (and in my view, unhelpful) regulatory descriptors: ‘service evaluation’ and ‘surveillance’, muddying the water further [5]. Hence, my plea that from an ethical point of view, the focus should be on ‘what is being done, not what it is called’ [4, 6]. The studies of condition X and condition Y both involve collecting patients’ data and there are issues of consent and confidentiality that pertain, irrespective of the study’s label. A ‘research’ study exploring the association between patients’ age and white cell count is ethically less potentially harmful than an ‘audit’ or ‘service evaluation’ that looks at the criminal records of nursing staff, even though both only involve examination of existing data. Much of this discussion hinges around the need for ethical approval (in the UK, by a Research Ethics Committee (REC) and in the US, by an Institutional Review Board (IRB)). Dr Shafer proposes that all ‘research’ must have IRB approval and all ‘audit’ submitted for publication is thereby immediately reclassified as ‘research’ and therefore must also have IRB approval. Here, we are both more in agreement than disagreement, and we come down to semantics. From an ethical point of view, it’s generally accepted that although an individual has the right to choose which ‘projects’ he supports, an organisation providing a service can analyse data about its service in order to maintain and improve standards, without necessarily having to ask its customers/clients for permission. Indeed, healthcare organisations have a moral and legal obligation to analyse the service they provide for patients, and patients implicitly accept this when receiving the service. Taking a local audit or survey and publishing it in an international journal, or presenting it at an international meeting, could be argued to exceed that understanding – the promotion of ‘generalisable knowledge’ to which Dr Shafer refers. Dr Shafer states that Anesthesia and Analgesia’s policy will be to insist on IRB approval for all audits before they can be considered for publication, and I can understand that viewpoint – although I do not think that one needs to invoke definitions such as ‘generalisable knowledge’ or ‘systematic investigations’ to reach that position. However, there is another factor that has relevance to readers of Anaesthesia more than to those of Anesthesia and Analgesia: in the UK, investigators are actively prevented from seeking ethical approval for anything other than studies classified as ‘research’ [7]. Since the only bodies (currently) than can give ethical approval for studies in the UK are RECs, and since anything ‘not research’ cannot be submitted to a REC (or to a NHS Research & Development Office either), projects that may have serious ethical issues, but that have the wrong name, are excluded from this process [6, 7]. Furthermore, RECs are not able to grant retrospective approval to a study already done. Thus Dr Shafer’s stipulation that an ‘audit’ can be submitted to Anesthesia and Analgesia only if it has been reviewed by an IRB and considered exempt from further review cannot apply in the UK, since a REC has no mechanism for reviewing it, even to the point of deciding to decline to review it! In that regard, investigators (and editors) should be envious of the situation in the US, where Dr Shafer’s ‘IRB sees all’ approach is more robust and clearer than on this side of the Pond. Both our journals have broadly similar requirements in terms of ethical requirements, although Anaesthesia’s reflect the situation pertaining in the UK (and some other countries too). Therefore, Anaesthesia will continue to consider studies defined as ‘non-research’ that have not been reviewed by the REC or equivalent, where such review is not available locally to investigators. In considering such studies, the Editorial Board will follow the guidance on this situation published by the Committee on Publication Ethics (COPE), i.e. taking into account the study’s scientific validity, presentation, ethical harms and potential benefits (including how these have been minimised/maximised respectively) [8]. This does not mean lower ethical standards, but reflects different regulatory structures. Investigators would be well advised to discuss these studies with their REC chairman (though the latter’s opinion regarding the need for ethical review does not constitute ‘approval’) and their trust’s Caldicott Guardian (in the NHS, the person responsible for protecting patients’ data), or their equivalents, before submitting such manuscripts to Anaesthesia. Our positions on consent are also broadly similar, but Anaesthesia will, rarely, consider case reports where consent (or assent) was not written. In such cases, as for ‘non-research’, each report will be reviewed on its merits, considering its harms, benefits, whether consent/assent could have been obtained, and the implications of the Data Protection Act [9]. I end on something about which we are in clear agreement: the Editorial Board of Anaesthesia, like that of Anesthesia and Analgesia, reserves the right to reject a manuscript irrespective of the local IRB’s or REC’s opinion. Vive la difference! I am Editor-in-Chief of Anaesthesia and a member of COPE Council and of Wiley-Blackwell’s Ethics Advisory Panel. No external funding declared.

ObjectiveTo describe influenza laboratory testing and results in the Military Health System and how influenza laboratory results may be used in DoD Electronic Surveillance System for Early Notification of Community-based Epidemics (ESSENCE)IntroductionTimely influenza data can help public health decision-makers identify influenza outbreaks and respond with preventative measures. DoD ESSENCE has the unique advantage of ingesting multiple data sources from the Military Health System (MHS), including outpatient, inpatient, and emergency department (ED) medical encounter diagnosis codes and laboratory-confirmed influenza data, to aid in influenza outbreak monitoring. The Influenza-like Illness (ILI) syndrome definition includes ICD-9 or ICD-10 codes that may increase the number of false positive alerts. Laboratory-confirmed influenza data provides an increased positive predictive value (PPV). The gold standard for influenza testing is molecular assays or viral culture. However, the tests may take 3-10 days to result. Rapid influenza diagnostic tests (RIDTs) have a lower sensitivity, but the timeliness of receiving a result improves to within <15 minutes. We evaluate the utility of RIDTs for routine ILI surveillance.MethodsAdministrative medical encounters for ILI and influenza laboratory-confirmed data were analyzed from the MHS from June 2013 – September 2017 (Figure 1). The medical encounters and laboratory data include outpatient, inpatient, and ED data. The ILI syndrome case definition is a medical encounter during the study period with an ICD-9 or ICD-10 codes in any diagnostic position (ICD-9 codes = 79.99, 382.9, 460, 461.9, 465.8, 465.9, 466.0, 486, 487.0, 487.1, 487.8, 488, 490, 780.6, or 786.2; ICD-10 codes = B97.89, H66.9, J00, J01.9, J06.9, J09, J09.X, J10, J10.0, J10.1, J10.2, J10.8, J11, J11.0, J11.1, J11.2, J11.8, J12.89, J12.9, J18, J20.9, J40, R05, R50.9). The ILI dataset was limited to care provided in the MHS as laboratory data is only available for direct care. We describe influenza laboratory testing practices in the MHS. We aggregated the ILI encounters and RIDT positive results into daily counts and generated a weekly Pearson’s correlation.ResultsInfluenza tests are ordered throughout the year; the mean weekly percentage of ILI encounters in which an influenza laboratory test is ordered is 5.62%, with a range from 0.68% in the off season to 19.2% during peak influenza activity. The mean weekly percentage of positive influenza laboratory results among all ILI encounters is 0.82%, with a range from 0.01% to 5.73% (Figure 2). The percent of ILI encounters in which a test is ordered increases as the influenza season progresses. Influenza laboratory tests conducted in the MHS include RIDTs, PCR, culture, and DFA. Among all influenza tests ordered in the MHS, 66.0% were RIDTs, 22.7% were PCR, and 11.3% were viral culture. Often, a confirmatory test is ordered following a RIDT; 20% of RIDTs have follow-up tests. The mean timeliness of influenza test result data in the MHS was 11.26 days for viral culture, 2.94 days for PCR, and 0.11 days for RIDTs. The RIDT results were moderately correlated with ILI encounters for the entire year (mean weekly Pearson correlation coefficient rho=0.60, 95% CI: 0.55, 0.66, Figure 3). During the influenza season, the mean weekly Pearson correlation coefficient increases to rho=0.75, 95% CI: 0.70, 0.79.ConclusionsThe DoD has the unique advantage of access to the electronic health record and laboratory tests and results of all MHS beneficiaries. This analysis provides evidence for increased utilization of positive RIDTs in ESSENCE. The moderate correlation between the ILI syndrome and positive RIDTs may be associated with ICD-10 codes included in the ILI syndrome definition that contribute to false positive influenza cases. Ongoing research is focused on improving this ILI syndrome definition using ICD-10 codes. Rapid influenza diagnostic tests provide more timely results than other influenza test types. In conjunction with ILI medical encounter data, positive RIDT data provides a more complete and timely picture of the true burden of influenza on the MHS population for early warning of influenza outbreaks.

During the onset of 2009 pandemic influenza A (H1N1) (pH1N1), the New York City Department of Health and Mental Hygiene implemented a pilot respiratory virus surveillance system. We evaluated the performance of this pilot system, which linked electronic health record (EHR) clinical, epidemiologic, and diagnostic data to monitor influenza-like illness (ILI) in the community. Surveillance was conducted at 9 community health centers with EHRs. Clinical decision support system alerts encouraged diagnostic testing of patients. Rapid influenza diagnostic testing (RIDT) and multiplex polymerase chain reaction assay (MassTag PCR) were performed sequentially. Nine Institute for Family Health (IFH) clinics in Manhattan and the Bronx during May 26 to June 30, 2009, the pH1N1 outbreak peak. Adult and pediatric patients presenting to IFH clinics during May 26 to June 30, 2009. By using Centers for Disease Control and Prevention guidelines, we evaluated the system's completeness, sensitivity, timeliness, and epidemiologic usefulness. Of 537 ILI visits (5.7% of all visits), 17% underwent diagnostic testing. Of the 132 specimens with both a RIDT and MassTag PCR result, 90 (68%) had a MassTag PCR-identified respiratory virus, most commonly pH1N1 (n = 69; 77%). Of the 81 specimens that met the ILI case definition, 58 (72%) were positive for a respiratory virus tested for by MassTag PCR; 48 (59%) were positive for pH1N1. Ninety-four percent of ILI patients positive for pH1N1 were 45 years or younger. Sensitivity and specificity of RIDT (29% and 94%) and ILI case definition (70% and 48%) for pH1N1 were calculated using MassTag PCR as the standard. Results of RIDT took a median of 6 days. Despite low RIDT sensitivity for pH1N1 and limited timeliness, integration of EHR and diagnostic data has potential to provide valuable epidemiologic information, guide public health response, and represents a new model for community surveillance for influenza and respiratory viruses.

Research ethics committees (RECs) in the UK National Health Service (NHS) have a long history and have evolved considerably in the past 20 years. While we write in an independent capacity, we also write as one chair of an NHS REC and we are both active nursing researchers engaged in NHS research. Therefore, we represent slightly different views but with sufficient concern in common for public and patient protection through ethical scrutiny of research but also for the facilitation of excellent research in the NHS. Research ethics committees exist to protect research participants, including patients, their relatives, NHS staff and others from harm as a result of participating in research. That harm might include physical, emotional, economic and/or social harm, and the possibility of that harm occurring can range from ‘no possible risk’ to ‘certainty of harm’. Research involving human participants can seldom, if ever, be described as having no risk, and a REC would be extremely unlikely to approve research where there is ‘certainty of harm’ to participants. Most research falls somewhere between the above two categories and does present some degree of risk. Under the direction of the Central Office for Research Ethics Committees (COREC) and its successor, the National Research Ethics Service (NRES), RECs have changed significantly in the past 20 years. In the 1990s, there were more than 200 NHS RECs in the UK, each with their own ways of working and their own application requirements. The result was an extremely complex muddle of ethical review across the NHS, causing considerable frustration to researchers. COREC and then NRES formalised and standardised ethical review and created a service fit for purpose in the UK. The result is that there are now fewer than 90 RECs in the UK, each performing to the same set of Standard Operating Procedures, which provide an ethical review process that is recognised as amongst the best in the world. As a result of unethical and well-publicised events occurring at Alder Hey in the 1990s, the NHS published the Research Governance Framework for Health and Social Care, which resulted in a new approach to research governance in the NHS and a requirement that all researchers also seek governance approval alongside ethical approval. The history of research ethics has been repeatedly tainted by a cycle of unethical research activities, often resulting in harm to those involved, followed by a reaction, with the aim of preventing a repeat of the unethical practice and resulting harm. Unfortunately, this worthy objective has seldom been achieved. For example, the Nuremberg Code and the later Declaration of Helsinki were published to ensure that the unethical and destructive research practices could never happen again. Despite worldwide adoption, these did not end unethical research practice, as clearly demonstrated by the writings of Henry K. Beecher and Maurice Pappworth and by many examples of unethical and harmful research practice, including the events at Alder Hey. The need for researchers to seek a favourable ethical opinion and research governance approval have gone a long way to ensuring the highest standard of ethical and safe research practice in the UK. There have also been improvements in the coordination, management and practice of ethical review and governance, but there is persistent criticism of the increased bureaucracy that has been introduced. This has resulted in additional workload for researchers in the NHS and an unprecedented complexity to the regulatory requirements with which researchers must comply. One of the main criticisms of the current requirements is that all research is treated in the same way, even when the risks associated with the research might be very different. Clinical trials of new medicines, where there might be considerable potential risks, are treated much the same as surveys seeking the opinions of respondents, where the risks might be negligible. The recent introduction of a process for proportionate review might have gone some way to fast-track low risk research through the ethical review process but this relates more to the time required for the review than to the paperwork burden placed on researchers. It is time that serious questions were asked about who is being protected by the current regulatory processes? Has the current process gone beyond the original purpose of ethical review and latterly, research governance: the protection of research participants and society from harmful research? Researchers now spend much of their time justifying their research to RECs and Research and Development (R&D) Departments. As ethical approval cannot be obtained without scientific review, it is normally the case that the time taken to gain ethical approval, which is not entirely predictable, erodes research time on projects and a considerable proportion of research funding is spent obtaining ethical approval. The shorter the project, the greater the proportion of time; for a 6 month project, a delay in obtaining ethical and R&D approval can jeopardise the study. Often this is on the basis of nothing related potential harm to patients but to additional layers of form filling for indirect aspects of the research such as financial transparency and data protection which are standard aspects of project management in which all researchers will be trained. Scrupulous researchers do not want to cause harm so should RECs be more trusting of researchers and move away from requiring them to pass burdensome and bureaucratic tests of form filling and document preparation? There has been an experiment with Research Passports but, apart from the extreme difficulty in obtaining one, some researchers report that this has made no difference to the ethical and R&D processes. Sadly, with policy and practice related to public protection, a ‘ratchet effect’ operates. Once a level of ‘protection’ has been reached, subsequent attempts to alter it lead to even more stringent procedures. However, like all ratchets, the system reaches a point where it can no longer function; how much longer before the process of seeking ethical approval for research in the NHS meets that point?

The integration of data analytics in healthcare has revolutionized the industry, ushering in a new era of personalized and patient-centric approaches to healthcare delivery. This review explores the multifaceted role of data analytics in reshaping the landscape of healthcare, with a specific focus on patient-centric methodologies and their impact on healthcare delivery. The advent of electronic health records (EHRs) and wearable devices has generated an unprecedented volume of healthcare data. Leveraging advanced analytics, healthcare providers can extract valuable insights from this data to enhance patient care. Patient-centric approaches involve the utilization of individualized health data to tailor treatment plans, predict disease outcomes, and optimize preventive measures. This review delves into the methodologies employed in patient-centric data analytics, examining the utilization of machine learning algorithms, predictive modeling, and artificial intelligence to develop personalized healthcare interventions. Furthermore, the review explores the transformative impact of data analytics on healthcare delivery. The optimization of operational processes, resource allocation, and the identification of cost-effective interventions are vital components of healthcare management. By harnessing the power of data analytics, healthcare systems can streamline their operations, reduce inefficiencies, and allocate resources more effectively. Additionally, predictive analytics aids in forecasting disease outbreaks, enabling proactive measures for containment and resource allocation. The review also highlights the ethical considerations and challenges associated with the implementation of data analytics in healthcare. Patient privacy, data security, and the responsible use of sensitive health information are critical aspects that demand careful attention in the era of digital healthcare. This review underscores the pivotal role of data analytics in fostering patient-centric healthcare approaches and optimizing healthcare delivery. As the healthcare industry continues to evolve, the integration of advanced analytics promises to revolutionize the way healthcare is administered, ensuring a more personalized, efficient, and effective approach to patient well-being.

The integration of data analytics into healthcare systems has revolutionized the management, delivery, and quality of healthcare services. This study systematically reviews and synthesizes findings from 105 peer-reviewed articles to explore the transformative role of data analytics in enhancing diagnostic accuracy, improving operational efficiency, managing public health crises, and addressing health disparities. Machine learning and artificial intelligence emerged as significant tools in supporting early diagnosis of chronic and acute diseases, improving clinical decision-making, and streamlining hospital workflows. Real-time analytics systems have been instrumental in managing healthcare resources, monitoring hospital capacities, and responding effectively to pandemics such as COVID-19. The integration of big data analytics with electronic health records (EHRs) has optimized resource allocation, reduced redundant processes, and lowered operational costs. Additionally, emerging technologies such as blockchain and Internet of Things (IoT) devices have addressed challenges related to secure data sharing, interoperability, and real-time health monitoring. Despite these advancements, the review identifies critical gaps, including the lack of long-term evaluations of analytics systems and scalability challenges in low-resource settings. These findings emphasize the need for robust infrastructure, ethical frameworks, and scalable solutions to ensure the widespread adoption and equitable use of healthcare analytics.

Information Technology is a significant portion of the healthcare system. Availability, integrity, security, and accuracy of the data in every healthcare process are vital. So, such data should be updated to fulfil continued improvement of the services in each healthcare providers and especially in healthcare. Thus, several information systems must be integrated with the healthcare systems. Healthcare record includes some information such as specific allergies and medications, medical history, the status of immunization, radiology images, results of lab and examination, everyone stat such as weight and age, appointments, order tests, and diagnoses. This record is identified by patient ID. The authorized hospital employees and the doctors will use their password and ID to login the application for privacy and security. Then a request for that patient record will be sent by using the patient ID, that will be recieved by the doctor, the recording ought to be recent since the patient final visitation to any Health Care (HC) organization in Kingdom of Saudi Arabia (KSA). So, the patient has a single Electronic Health Record (EHR), and this will reduce the cost and time of patient, and support doctors to obtain proper and recent information of any patient from the recordings from any HC organization in KSA. Also, EHR gives a better healthcare of patients that averts some medical errors due to the information lack and health records unavailability. A small number of HC organizations have more advanced EHR adoption, where the most of them has no EHR at all. So, we proposed a Health Cloud Framework (HCF) to offer a comprehensive EHR unified system, by applying the cloud computing technology on EHR system. Where, the paradigm of cloud computing is a latest appearing technology utilized in industries and achieved a considerable gaining. Regardless of the large characteristics of cloud computing, they haven’t been used rightly so far, in the medical industries. HCF proposed to get better the normal process of acquiring all patient health recordings distributed in each HC organization any time using portable devices, based on cloud computing technology oriented architecture.

Technical Issues Encountered During Ethics Review: Quality Assurance in the Research Process

This comprehensive article explores the transformation of healthcare delivery through advanced workflow models and digital solutions, emphasizing the critical role of technology integration in modern healthcare systems. The article examines various aspects of healthcare workflow optimization, including clinical processes, administrative workflows, patient engagement systems, and cross-departmental coordination. It shows the implementation of digital healthcare solutions, focusing on Electronic Health Records (EHR) interoperability, telemedicine advancements, mobile health applications, and patient portal development. The article analyzes process optimization and automation strategies, highlighting the significance of artificial intelligence, machine learning, and data analytics in healthcare operations. Through detailed examination of implementation best practices, the article addresses staff training, change management, technology integration, risk mitigation, and quality assurance methods. The article concludes with an evaluation of measuring impact and future directions, providing insights into key performance indicators, success metrics, and emerging technologies in healthcare delivery systems. This holistic approach to healthcare workflow optimization offers valuable insights for healthcare organizations seeking to enhance operational efficiency, improve patient care, and adapt to evolving technological landscapes.

IntroductionThe ability to utilize the electronic health record (EHR) without compromising the doctor-patient relationship (DPR) is an essential skill of all physicians and trainees, yet little time is spent on educating or assessing learners on needed techniques. To address this gap, we developed a conventional OSCE station coupled with a simulated patient chart within the Epic program in order to assess our students' skills utilizing the EHR during a patient encounter.MethodsOf third-year medical students, 119 were given full access to the patient's simulated chart 24 hours in advance of their OSCE to review clinical data. During an in-person OSCE with a standardized patient (SP), students performed a focused history and physical, using the EHR to verify allergies and medications. Students completed an electronic patient note graded by faculty. SPs evaluated the students on communication and interpersonal skills with specific rubric elements. Faculty graded the students' notes to evaluate their expression of clinical reasoning in the assessment and plan.ResultsTraining SPs and faculty to assess students on EHR skills was feasible. After implementation of a comprehensive curriculum focused on EHR and DPR, there was a significant difference on EHR-related communication skills (M = 76.4, SD = 17.6) versus (M = 37, SD = 28.9) before curriculum enhancement t (117.9) = −12.4, p <.001.DiscussionThe EHR OSCE station provided a standardized method of assessing students' EHR skills during a patient encounter. Challenges still exist in the technological requirements to develop and deliver cases in today's EHR platform.

Ethical approval, laws and publication of caring science in the <scp>N</scp>ordic countries

e-Liability and e-Documentation.

The adoption of Electronic Medical Records (EMR) in Nigeria has been slow despite the benefits. Health worker perception is pertinent in promoting its adoption. To assess the perception and barriers to EMR use among physicians and nurses in general hospitals in Lagos, Nigeria. This was a descriptive cross-sectional study utilizing multistage sampling to select 293 respondents. Data was collected using a self-administered questionnaire, analysed and presented as frequency tables. Chi-squared test determined association with EMR perception with level of significance at p ≤ 0.05. The mean age of the respondents was 36.6 ± 10.2 years. Majority of the respondents (69.3%) were female. Most respondents (98.6%) had positive perception of EMR. Major barriers to the use of EMR highlighted by the respondents included insufficient computers (90.8%), inconsistent power supply (87.0%), hardware or software failure (85.7%), and poor internet (84.7%). There was significant association between respondents age group and EMR perception (p = 0.018). There was a predominantly positive perception of EMR among healthcare workers in Lagos, despite significant barriers such as inadequate infrastructure and inconsistent power supply. Strategic investments in technology, reliable electricity, and internet connectivity are needed for enhancing EMR adoption and optimizing healthcare delivery.

Not available.