Knowledge Management – What Is Its Role in Healthcare Research?

This week, we are diving into knowledge management, another new concept we can both learn together.

The driving question is…

How can knowledge management improve access to healthcare research?

To answer the question above, we were tasked to pick a local public health problem and answer the driving question in relation to the public health problem.

Let me break it down and introduce a few concepts first.

Knowledge management (KM) is a concept that was first introduced roughly around the 1990s (Koenig, 2012). According to the Association of State and Territorial Health Officials or ASTHO (2005), is defined as “a process used by organizations and communities to improve how business is conducted by leveraging data and information that are gathered, organized, managed, and shared.” The Gartner Group (2016) defined it as “business process that formalizes the management and use of an enterprise’s intellectual assets. [Knowledge Management] promotes a collaborative and integrative approach to the creation, capture, organization, access and use of information assets, including the tacit, uncaptured knowledge of people.” Its goal is to move from “not knowing what you know” and using that knowledge to improve organizational effectiveness and efficiency (ASTHO, 2005). The building blocks of KM are data, information and knowledge, which are represented by the figure below:

screen-shot-2016-11-28-at-12-12-25-am

Data is defined as “unprocessed representations of raw facts, concepts or instructions that can be communicated, interpreted, or processed by humans or automatic means (ASTHO, 2005).” An example of data is the number of dengue cases in a region. When meaning is assigned to data or when data is categorized, filtered or indexed, it now becomes information. In the dengue scenario, an example of information would be the number of dengue cases per district or barangay in the region. Finally, when processes like critical thinking, evaluation, structure or organization are applied to support decisions or understand concepts, information becomes knowledge. Knowledge is dynamic and evolving. It also has two types, namely: explicit and tacit. The former is also known as ‘book knowledge’ and refers to the “ordering of data and information according to well-defined, formalized procedures or rules.” On the other hand, the later refers to knowledge that is informal and that which is gained through experience and training. An example of knowledge in the dengue scenario is understanding the patterns of illness well enough to adjust preventative measures in a particular area.

When pieces of information are linked in meaningful ways, the information’s relevance to the problem at hand is established, and the information at hand is understood in larger context, there is translation of information to knowledge. This can then lead to knowledge translation which occurs when knowledge is put into action. (Straus et. al, 2011). The concept of knowledge translation is especially important because despite the abundance of evidence-based data, it has been found that there is still large gap between what is known and what is used in practice. In addition, there is a failure to use health research evidence in making informed decision related to healthcare.

For an organization to adapt the KM approach, the following components must be examined: culture, content, processes and technology (ASTHO, 2005). Culture is the organization’s shared set of beliefs, values, and understandings, and therefore varies from one organization to another. It is reflected by how and organization envisions, measures, and carries out its mission and responsibilities. Content refers to resources of the organization, which can range from data to information to skills to expertise. Meanwhile, processes are methods by which an organization manages data and information. They can be formal or informal. The processes are in place to ensure that content is created, assessed, management and disseminated effectively. Finally, technology has to be assessed in the context of why and how effectively it is used in an organization.

The role of knowledge management in public health is critical. To do their jobs, public health practitioners require accurate data and the ability to access data quickly from different sources and transform said data into useful information and knowledge. The officials, in particular, need up-to-date information for them to conduct analyses, report and generate vital information, and to collaborate with other agencies. In addition, the knowledge generated will guide decision-making in addressing public health concerns. Through KM, there can be an efficient way of developing and disseminating best practices and of continually assessing said practices for improvement.

In order to illustrate KM further, let us use dengue as a public health concern as an example. The dengue epidemic has plagued the Philippines since 1953 and continues to be a significant public health concern (Interhealth Worldwide, 2016) despite the many efforts of the Department of Health.

There was a recent article published, however, that featured the elimination of dengue cases through the combined efforts of a data analyst, professor, and local agency director. The story was a feature piece in Manila Bulletin and the subheadline mentioned that the answer to the dengue problem was not medicine but big data. It recounted how Wilson Chua, a big data analyst, was able to analyze the raw data on the dengue cases in Pangasinan provided to him by the Department of Health. He noted that the district of Bonuan had the highest number of cases in Pangasinan for 3 consecutive years, and that most of those infected were children aged 5-15. In doing his research, he found out that there were two public schools in the area (specifically Barangay Bonuan Boquig) with large pools of stagnant water in between, which were assumed as the source of the dengue vector.  Once he was able to identify the problem, he crowdsourced through social media (Facebook in particular) and got in touch with Professor Nicanor Melecio, the project director of the e-Smart Operation Center of Dagupan City Government, and Wesley Rosario, director at the Bureau of Fisheries and Aquatic Resources. Their solution to the problem was two-pronged, which was release of mosquito dunks and mosquito fish. They were able to implement this through the help of the local government. Thirty days after intervention, there was still no report of dengue cases in the area (you can read the full story here).

This article caught my attention for several reasons. One, I found it amazing that they were able to find a solution that could help address the dengue problem in the country. While I am aware that their solution is not applicable to other areas (ex. urban poor areas where large pools of water are not present and mosquito fish release is not feasible), it still impresses me how they were able to collaborate and come up with a solution. Two, the initial efforts were not made by a public health professional but by an analyst who just happened to have a personal interest in the situation. Third, his collaborators were not from the Department of Health. Fourth, DOH gave him raw data. (It was mentioned in the article that he was given an file with 81,000 rows of data. Aren’t there data privacy concerns?) Lastly, it mentioned that ‘big data’ was the solution.

Let me take a quick detour and discuss that last point. Big data, according to Tech Target, is a term that refers to a voluminous amount of structured, semi-structured, or unstructured data that has potential to be mined for information. There is no consensus on what ‘voluminous’ equates to in terms of volume or size, although it often refers to terabytes, petabytes, or exabytes of data captured over time. It is often characterized by extreme volume of data, wide variety of data types, and the velocity with which the data must be processed.

Going back, it now makes me wonder about how DOH has handled the massive amount of data that they have. Have they thought of Mr. Chua and his team’s interventions before? Based on the article by Lomibao (2013) in the Philippine Inquirer, Mr. Rosario mentioned that mosquito fish has been in the country for decades. Mosquito dunks are also not a new concept. But is the two-pronged approach new? If it showed promising results in Pangasinan, are there efforts to disseminate the information and replicate it in regions with similar topography? Is further research being done to address the feasibility of this solution and its long-term effects?

Recalling the 4 components of KM, I’ll now discuss how each of those components would play a role in improving access to healthcare research given the scenario above. Organization in the following paragraph will refer to the Department of Health.

The ideal culture in an organization is one where leadership plays a strong role in establishing the cultural will to support and maintain practices such as data documentation and dissemination of results. Considering the nature of public health, leadership should also be able to effectively communicate and coordinate with other organizations. It will help in understanding how the practices of others might be leveraged. First, recognizing the efforts of Mr. Chua is imperative. Given his and his collaborators’ success, a more wide-scale feasibility study should be done on the intervention that they did. Long-term effects of said intervention should likewise be performed, since the Department of Environment and Natural Resources’ Protected Areas and Wildlife Bureau (PAWB) has already expressed their concern over the potential negative and irreversible impact on our fragile biodiversity (Lomibao, 2013). At least for this particular aspect of dengue vector control and management, the DOH should work with BFAR, PAWB and the local government.

For content, one of the most significant challenges is capturing tacit knowledge, which means making it easy for individuals to share what they know through training, collaborative opportunities, networking, and other personal interactions. Mr. Chua can be considered as a potential content resource of the organization. I assume that there are already people employed by the DOH who do what Mr. Chua does, but he can help by sharing his knowledge on how he analyzed the data that was given to him.

As for processes, the ideal scenario is that the data and information management processes that exist in the organization are driven by the needs identified from the agency’s business activities. Given the nature of the public health sector, the data it collects is usually shared with external organizations aside for its intended personal use. Because of this, data and information management processes should make it possible for the DOH to share their data in a way that is understable and meaningful to other sectors, such as the local government or BFAR.

Lastly, for technology, it is already a reality that the public health sector has been increasingly using electronic technology to collect, store, access, analyze, visualize and communicate data. However, there remains a gap in the availability of personnel who both have an expertise in information technology and in public health, or the so-called hybrids. If the gap in hybrids cannot be addressed for now since it needs specialized training, the DOH could continue its efforts to collaborate with experts in different fields to address the problem.

If the above core components are present in an organization such as the Department of Health, knowledge on dengue and means for vector control would be more accessible. It will also encourage and allow for further research to be done. 

That is it for week number 12 of HI 201. I have a couple of questions for you.

First, do you agree that big data was indeed the answer to the dengue problem (at least for Pangasinan)? Second, do you think your organization (if you belong to one) is ready to adopt knowledge management approach?

Let me know in the comments below.

XO,
Eve

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Clinical Decision Support Systems – How Can They Impact Healthcare?

Let’s skip the intro and dive right into this week’s assignment. The driving question that we needed to answer was…

“How can Clinical Decision Support Systems (CDSS) improve the quality of healthcare?”

Aside from the question above, we were tasked to think of a clinical scenario and suggest a clinical decision support system embedded within CHITS to address this.

A clinical decision support system or CDSS, according to Jaspers et. al (2010) is a tool that provides “clinicians or patients with computer-generated clinical knowledge and patient-related information, intelligently filtered or presented at appropriate times, to enhance patient care.” Another definition was provided by Souza et. al (2011) which describes CDSS as “computerized matching of an individual patient’s characteristics with a knowledge base that then provides patient specific recommendations to healthcare providers.” Common examples of CDSS are those that check for drug-drug interactions among the medications of a patient or those that check for possible drug-disease precautions or contraindications, reminder and recall systems for immunizations, prompts for  clinicians to initiate smoking cessation interventions, blood pressure screening, or laboratory/radiology testing among others. CDSS can exist independent of electronic medical records (EMRs) or may be incorporated into them. The idea is that CDSS will provide decision support to users at the time they make decisions, which should result in improved quality of care. For example, it can alert a physician that his/her medication of choice has a drug-drug interaction with a patient’s existing medication. Or it could also alert the healthcare practitioner (HCP) that his/her choice of medication can potentially aggravate a patient’s condition. Other examples are reminders such as the need for mammographic screening in women older than 40 or 45 years old, or the recommended vaccinations for an infant who is 14 weeks old. In the paper by Bates et. al (2003), they identified 10 “commandments” for an effective CDSS. I will enumerate and briefly explain them below.

  1. Speed is everything. As mentioned above, the overall goal of CDSS is to provide decision support at the time the decision is being made. Bates et. al has found that the speed of an information system is the parameter that users value most.  A good IS, therefore, will less likely be seen as helpful if it takes time for decision support to be shown or accessed. I imagine this would be more applicable to settings wherein there is a long queue of patients who need attention.
  2. Anticipate needs and deliver in real time. This pertains to making the necessary information available to clinicians at the time they need it. An example of which is emphasizing high potassium levels in a patient receiving a potassium-sparing diuretic in a patient with hypertension or congestive heart failure.
  3. Fit into the user’s workflow. In one of my earlier blogs, I mentioned that one of involvement of the end users as early as the planning or design phase is one of the determinants for success of a health information system. Part of involving users is making sure that the proposed IS will work well with their current workflow. A new software (or CDSS in particular), even when equipped with excellent guidelines, will likely meet resistance when it significantly changes the workflow of the end users (unless the workflow is the actual problem and it NEEDS to be changed).
  4. Little things can make a big difference. This sounds cliché to me but its applicability, even to CDSS, cannot be ignored. Little things in the CDSS can spell the success or failure of the system. For example, requiring a physician to enter his/her diagnosis using ICD codes (with a drop-down button) as opposed to entering it in free text will better help in ensuring that diagnoses for the patients are entered in a standard way. If part of the CDSS, then, is checking for drug-disease interactions, the system will be more accurate in delivering such information because the diagnoses are standardized.
  5. Recognize that physicians will strongly resist stopping. For example, your CDSS tells the physician that no further tests are recommended for the patient. However, the physician has been used to ordering a chest x-ray for similar cases. Since the CDSS only mentioned to stop but did not offer an alternative, the physician will most likely override the said recommendation and continue to order the chest x-ray. The same is applicable for medications, such that giving no or an undesirable alternative to the physician prescribing the medication will most likely make him/her prescribe the medication he had intended to.  
  6. Changing direction is easier than stopping. This is in relation to the previous number. An example of change would be changing the default dose and frequency of a drug when a physician orders it. Unless the physician has specific reason to change the dose and/or frequency, he/she will be more likely to use the suggested/default dose/frequency.
  7. Simple interventions work best. An alternative to this statement would be less is more. The most important guidelines for a medication, for example, should fit in the HCP’s screen. It might be helpful to have it linked to a more comprehensive or complete list of guidelines, which should also be readily accessible to the clinician with a few clicks.
  8. Ask for additional information only when you really need it. In CDSS that are integrated into the EMR, there are patient data that are already available for use by the CDSS to be able to give decision support. If the data is insufficient, the HCP will be asked to enter additional data. In stand-alone CDSS, most data will have to be entered by the HCP. According to Bates, et. al, “the likelihood of success in implementing a computerized guideline is inversely proportional to the number of extra data elements needed.” Therefore, it would be best if the least amount of data is asked of the HCP to be able to provide meaningful decision support, as a system that requires an HCP to enter several data will be something that a clinician is less likely to use, especially considering the usually limited amount of time HCPs have in dealing with their patients.
  9. Monitor impact, get feedback, and respond. How likely are clinicians to accept the suggestions of the CDSS? How often do they overlook the suggestions? How do their actions impact process flow, or more importantly, patient care and patient outcomes? It is essential to know the answers to these, and make corrective actions even mid-course to improve the CDSS.
  10. Manage and maintain your knowledge-based systems. CDSS suggestions are most often based on local/international guidelines. Since medicine is dynamic (and so are the guidelines), CDSS should also be able to keep up with the changes. A decision support with outdated guidelines will defeat the purpose. It is also helpful to track the response of the users to the CDSS and evaluate the reports on a regular basis.

Now that we know more about clinical decision support systems, let us discuss CHITS. CHITS stands for Community Health Information Tracking System. It was developed by the National Telehealth Center (NTHC) to improve health information management at the rural health unit level. It was described as an “EMR for the health workers, by the health workers” since it was developed alongside health workers and was designed in such a way that it features a workflow that is similar to what is currently being employed in local health centers. It uses an open source software making it more flexible to the needs of both the RHU and the DOH. It was built to improve data-gathering at the RHU level to be able to generate more accurate reports, which will be later used to plan programs and effect changes on both local and national public health levels.

I was a student nurse rotating in the local health centers (in Metro Manila and Batangas) from 2006-2009 and I was a medical intern doing community rotation from 2012-2014. I have never encountered CHITS in any of the health centers I rotated at. There is limited information on what CHITS is and what is available so far (based on my internet search), so I will assume that my suggestions below are still non-existent.

In this scenario, barangay health worker (BHW) Dela Cruz is on duty at a rural health center. It’s vaccination day and Mommy Anna comes in with her children Karen and Nina, who are 2 years old and 9 months old, respectively. BHW Dela Cruz  briefly interviews Mommy Anna and gets pertinent data regarding her children. During the interview, she found out that Mommy Anna had just moved into the barangay and that her daughters have never received any type of vaccination. She was then asked by the mother which vaccines her daughters should receive. In the brief period that Dela Cruz has been a BHW, she has never actually encountered an infant or toddler who is naive to any type of vaccination.

I think that a useful CDSS in this scenario would be something that guides a healthcare worker (HCW) on the vaccinations a patient, particularly a pediatric patient, should receive. The minimum data required would be the patient’s birthday (the age should be computed automatically). When the software determines that the patient is a pediatric patient, the recommended vaccinations for patient based on age should be displayed. The HCW can then enter the date the vaccines were received (if any and if they were received elsewhere). It is at this point that any previous reactions to vaccines should also be documented. If there are none, as in the scenario above, the CDSS should show the recommended vaccines arranged by priority. The basis for the recommendation would be the guidelines based on DOH’s expanded program on immunization (EPI). Contraindications for vaccines in general or for a specific vaccine could also be shown. A trained barangay health worker (BHW) should be able to do a simple assessment, enter the birthdate, and identify which vaccinations a patient should receive. Once the vaccine has been administered, it is documented in the software. Ideally, if the vaccine is part of a series, the date for the next dose should be shown so that the BHW could inform the patient (or his/her guardian) on when to come back. The same CDSS should also be able to generate reports such as the type and quantity of vaccinations given on a daily, weekly, monthly, quarterly or annual basis or how many children are considered fully immunized. These types of reports could be married with statistics from the local government to determine the vaccine coverage in the area to assist in procurement of additional vaccines and to help plan for program that are related to vaccination.

Again, I have to admit that I am not aware if this is already being done in the RHUs. Considering the lack of healthcare professionals in the rural health units, particularly the geographically isolated and disadvantaged areas, this type of CDSS would be very helpful to the RHU healthcare workers and in helping attain the main goal of DOH’s EPI, which is to reduce the mortality and morbidity among children against the most common vaccine-preventable diseases.

I personally am using a vaccination application on my smart phone that was created by the Centers for Disease Control (CDC). It has recommendations based on age group or based on special populations (pregnant, person with HIV, etc.). As a standalone app, it has been very helpful to my in my practice and for my own personal use. Something similar could be incorporated to CHITS to help implement the immunization program of DOH.

That is it for week 11 of HI 201! Can you think of other clinical decision support systems that could benefit people especially in the remote areas where there is often limited access to healthcare? What do you think are pitfalls of CDSS?

As always, let me know in the comments below. I would love to hear and learn from you!

Love,
Eve


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Standards and Interoperability – How Can These Be Adopted?

This week, we are diving further into the basics of health informatics. Our focus would be on interoperability, with the driving question…

How can healthcare institutions adopt standards to ensure interoperability?

We were tasked to pick among messaging, terminology, and imaging – the three broad categories of standards – and to prepare a master plan for its adoption in a government setting.

As always, let me start by defining some terms.

Standardization is defined by Investopedia as “a framework of agreements to which all relevant parties in an industry or organization must adhere to ensure that all processes associated with the creation of a good or performance of service are performed within set guidelines.” Adebesin, et. al. (2013) stated that this is the key to interoperability. eHealth Interoperability, in particular, is defined as the “ability of two or more systems or components and the business processes they support to exchange information and to use the information that has been exchanged.”

While there remains no consensus on the levels of interoperability, the European Telecommunication Standards Institute has identified 4 levels, namely: technical, syntactic, semantic, and organizational. Technical interoperability enables heterogeneous systems to exchange data. Syntactic interoperability, on the other hand, guarantees the preservation of the clinical purpose of the data during transmission among healthcare systems. Meanwhile, semantic interoperability is that which enables multiple systems to interpret the information that has been exchanged in a similar way through predefined meaning of shared concepts. Finally, organizational interoperability, the highest level of interoperability, facilitates the integration of business processes and workflows beyond the boundaries of a single organization.

I would like to focus on semantic interoperability. To me, when simply put, this means that different systems understand each other. It’s like different people speaking and understanding each other using English, for example, despite the fact that they have different nationalities. Of the three broad categories of standards, semantic interoperability focuses on terminology.

One example of a terminology standard is SNOMED-CT. The International Health Terminology Standards Development Organisation (IHTSDO) owns and distributes SNOMED-CT. The claim is that it is the world’s most comprehensive multilingual clinical healthcare terminology that enables consistent, processable representation of clinical content in electronic health records. Its goal is to facilitate the accurate recording and sharing of clinical and health-related information and the semantic interoperability of health records. The way it works is that the content is represented using three types of components – content, description, and relationship. First, the concepts in SNOMED-CT, which represent clinical thoughts, have a corresponding unique identifier. Each concept is also associated with a single unambiguous Fully Specified Name (FSN) that contains the semantic tag and identifies the hierarchy to which the concept belongs. A concept can have one or more descriptions, and descriptions also have a unique description identifier. Finally, relationships link concepts together and provide formal definitions and other properties of the concept. Below is an example of how the components are interrelated:

screen-shot-2016-10-25-at-11-32-46-pmIn more plain and basic terms, SNOMED-CT is like a comprehensive dictionary where health data are defined, and at the same time functions as a thesaurus where synonyms of terminologies can be found (although SNOMED-CT is definitely more than this).

When incorporated into an electronic health record, for example, it allows uniform entry and interpretation of data. The encoded data can later be reused and presented for a variety of purposes. For example, clinical records represented using SNOMED-CT can be processed and presented in different ways to support direct patient care, clinical audit, research, epidemiology, management, and service planning.

IHTSDO has been working with other standards bodies like the ISO and HL, and they map from and to other code systems. As mentioned earlier, it is also multilingual. These ensure not only the applicability of SNOMED-CT to different disciplines but it also reinforces its global applicability. At the same time, it is also scalable and flexible and could adjust to local requirements, if necessary.

In the local setting, part of the Department of Health has already outlined the Philippine eHealth Strategic Framework and Plan (PeHSFP) 2014-2020. We are developing our own National Health Data Dictionary, which will function similar to SNOMED and will be integral to the implementation of the Philippine Health Information Exchange (PHIE).

hie-2The above image illustrates the PHIE. Note that on the upper right corner, under health data standards, is terminology. This is where the National Health Data Dictionary comes in. It will be used to ensure good data quality among the different systems that will be integrated in the PHIE.

If a government hospital, then, needs to adopt this standard, how will they do so? In a previous blog, I enumerated and briefly described the 7 components of eHealth projects according to the PeHSFP, namely: (1) governance; (2) legislation, policy, and compliance; (3) standards and interoperability; (4) strategy and investment; (5) infrastructure; (6) human resource, and; (7) eHealth solution.

Because we are talking about a government hospital, most of the components will be coming not just from the board or the hospital administrators but through mandates of the national and local government. For example, the creation of the National Governance Steering Committee and Technical Group on eHealth and the National eHealth Governance Implementing Policies, Procedures and Guidelines will be through creation of administrative orders. The same applies for other mandates that will come from the national government. Examples of which are the National Implementation of Health Data Standards for eHealth Standardization and Interoperability, the Implementation of Software Data Compliance to National Health Data Reporting, Implementation of Philippine Health Information Exchange and its Implementing Policies, Procedures and/or Guidelines, and creation and updating of the National Health Data Dictionary. To comply, a government hospital would then implement said administrative orders. The electronic health system they will use will most likely be a software that was developed for government hospitals in the Philippines. In that scenario, the said system would also have already been compliant with the parameters set forth by the Software Data Compliance Body. In addition, encoding of data would also make use terminologies of the National Health Data Dictionary.

Let’s say, however, that a government hospital, for whatever reason, was not required to be part of the PHIE but would like to become one. It still has to start with governance, who will decide on the kind of eHealth system that the hospital will use. They should create the necessary policies and implementing orders for the said project, which would be better handled by a specialized team dedicated to creating, implementing, and maintaining the system. Part of the planning and design for the project would already involve sourcing for funds for the project, hiring and training the right people, and ensuring that the system has adequate infrastructure to run the software. Specific to terminology and in effect, ensuring data quality, is integrating the coded terminologies under the National Health Data Dictionary into the software. This will make the software interoperable with the other systems that are likewise part of the PHIE.

In ensuring interoperability in general, there has been a lot of discussion on the use of open source software. As the name implies, open source in terms of software development and licensing means that the source code is accessible and modifiable by the end user. This is in contrast to proprietary software wherein the source code is confidential and the end user can access and execute only the machine code. The argument, according to Reynolds and Wyatt (2012), is that “open source software (OSS) licensed HIS provide a key opportunity for the promotion of effective systems by enhancing clinical engagement in software development, fostering innovation, improving system usability, and reducing costs, and should therefore be central to a rational HIS procurement strategy.” With OSS, there will be lower entry barriers and developers would have to compete with implementation of the same standards. The ultimate beneficiary of these would be the consumers, since use of OSS would encourage innovation and lowered cost from developers. There would also be no fear of lock-in, and the consumers can freely switch to another product without losing their data. A real-life example of this would be the use of the Android operating system. The Android operating system is based from the Android Open Source Project (AOSP). There are a variety of mobile phones operating using Android OS, and a user can switch from one brand of cellphone to another and bring their data along with them, since there are numerous applications that run using the said OS. In the healthcare setting in the Philippines, this would translate to developers using the same source code in the development of their eHealth systems. The health data would be structured in the same way, even if different softwares are used. With the use of a common health data dictionary to code for the data to be entered into the system, the terminology would be the same as well. This means that even if a healthcare institution discontinues the use of a particular software and later on switches to another one, as long as the said software used is running on a similar source code or is compliant with the standards set forth by the PHIE, migration would be possible.

And that is it for this week’s blog! I had a rather difficult time understanding the concepts but I hope I was able to make some sense. I feel that as the course progresses and as I make more blogs, things are starting to become more cohesive. It’s also the reason why I’ve been referring to the content of previous blogs more often (especially that of PHIE). Let me know your thoughts and I hope we can discuss!

Love,
Eve


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